TW201830320A - Method of learning language - Google Patents

Abstract

A method of language learning that allows users to understand the words through learning different related words or phrases in an interactive way, including through the sentences. For example, to learn the meaning of "apple", including some related words, such as through the word of "fruit", to understand that "Apple" is a "fruit". Through the word of "tree" and their relationship to understand that "Apple" grows on the "tree". Therefore, this invention enables users to learn each words and phrases through various words in different relationships and orders, to train children in developing language comprehension and logic thinking.

Description

   Language learning methods   

The present invention relates to a method of learning Chinese, which needs to be implemented with a computer.

A method for teaching children to learn Chinese, for example, US-patent US7563099, a multi-media method and apparatus for teaching language, discloses a series of images showing an object, the images including different However, comparable objects can also be accompanied by voice descriptions of such objects. Such teaching can increase the impression of objects.

The method and apparatus for developing cognitive skills of the US patent US 7933852 display images, users are asked to answer the images, and the images can be repeatedly displayed to help students understand the cognitive ability.

However, for the understanding of cognitive ability, the previous case did not allow users to understand the words or words to be learned from different words or words in an interactive manner. For example, understanding the "table" can be understood by the "wood" "Materials" can use "wood"; "chairs" can also be used to understand "tables" because "chairs" are often used with "tables"; or "table lamps" can be used to understand "tables" because "table lamps" are often used put it on the table.

The previous cases did not provide a way for users to interact with different objects such as text, images, and speech through learning methods that understand the above knowledge, to help students' cognitive ability and understand the logical relationship between words and sentences.

The main purpose of the present invention is to provide a language learning method that allows users to understand the words or words they want to learn from different words or words in an interactive manner, including through sentences, for example, to learn the meaning of "Apple", including through Some related words to learn, for example, to understand "Apple" through the relationship between "Fruit" and "Apple". At this time, you can understand that "Apple" is a type of "Fruit". Another example is to understand that "Apple" grows on "Tree" through the relationship between "Tree" and "Apple". Therefore, the design method of the present invention allows users to recognize words or words from different angles, and can also train children to think differently.

To achieve the above object, the present invention provides a method for language learning. First, a main object (such as Apple) and at least one related object are displayed on the screen, and preferably, a plurality of related objects are displayed. Plural related objects may, for example, surround the main object. Objects can be text or images, or text and images.

When the user selects a related object, a combination sentence is selected. The combination sentence includes the text of the main object (such as Apple) and the text of the related object (such as Fruit). The combination sentence is, for example, Apple is a fruit.

According to an embodiment, the main object, the related object and the combined sentence are displayed, so that the user can understand the main object through the related object and the combined sentence.

If the user selects a related object (such as Fruit), a new master object is generated (such as Fruit) and a new related object is also generated. In this way, new main objects and new related objects are continuously generated, and users can gradually learn more words, words, and sentences according to their interests. Moreover, the related objects are gradually extended to the outside, that is, each user can follow Learn according to your own interests, your own level, and your own pace. The present invention is suitable for various users through the generation of dynamic content.

In the present invention, a database is required to operate. The database can be constantly updated. It is best to introduce big data analysis to calculate the relevance of each object, or the weight of the relevance of each object in the combined sentence, etc. Can be getting smarter.

According to an embodiment of the present invention, a movie can also be put in. After the movie is played, a plurality of candidate main objects can be displayed, so that the user can select one of them as the main object, so that the user can access pictures, sentences, and Can learn through videos.

According to an embodiment of the present invention, an interactive question and answer can also be put. After each user gradually learns from related objects in a step-by-step manner according to his own interests and degrees, the present invention records the single-layer or multi-layer related objects learned by the user, and provides learners with repeated exercises and interactive questions and answers. Interactive Q & A can understand the user's ability to recognize different objects such as text, images, and speech, and can also test whether learners understand the logical relationship between words and sentences.

1‧‧‧ computer

10‧‧‧ near-end computer

11‧‧‧ processor

12‧‧‧Memory

13‧‧‧Screen

14‧‧‧ Horn

15‧‧‧input device

15m‧‧‧microphone

16‧‧‧Communication device

20‧‧‧ remote server

21‧‧‧Processor

22‧‧‧Memory

26‧‧‧Communication device

30‧‧‧ Language Learning Software Program

30s‧‧‧ language learning software program

31‧‧‧Database

31s‧‧‧Database

31a‧‧‧text

31b‧‧‧ related words

31c‧‧‧word

31d‧‧‧degree

31e‧‧‧Learning times

31f‧‧‧ voice file

31g‧‧‧Picture

31h‧‧‧Video file

31i‧‧‧Video Tags

31j‧‧‧weight

31k‧‧‧related

41‧‧‧ the first main object

41t‧‧‧first main text

41g‧‧‧first main image

41v‧‧‧ Video

43‧‧‧ first related object

43t‧‧‧first related word

43g‧‧‧First related image

43a‧‧‧First target related object

45‧‧‧ the first combination of words

45a‧‧‧ first combination alternative sentence

45b‧‧‧Second Combination Alternative

47‧‧‧Second Master Object

47t‧‧‧Second main text

47g‧‧‧Second main image

49‧‧‧ second related object

49t‧‧‧second related word

49g‧‧‧Second Related Image

50‧‧‧ Candidate Master Object

50t‧‧‧candidate for main object

51‧‧‧ compare target image

51a‧‧‧corresponding image

51b‧‧‧candidate image

51c‧‧‧candidate image

51d‧‧‧candidate image

51e‧‧‧candidate image

71‧‧‧First choice instruction

72‧‧‧Second selection instruction

73‧‧‧Third choice instruction

74‧‧‧Fourth selection instruction

75‧‧‧Fifth selection instruction

76‧‧‧ Sixth selection instruction

80‧‧‧ Video

90‧‧‧ users

95‧‧‧Internet

131‧‧‧First display interface

132‧‧‧Second display interface

133‧‧‧ Third display interface

134‧‧‧Fourth display interface

135‧‧‧Fifth display interface

FIG. 1 is a hardware architecture diagram of the present invention.

Fig. 2 is a flowchart of the present invention.

FIG. 3 is a schematic diagram of a process for generating various objects according to the present invention.

FIG. 4 is a schematic flow chart of generating instructions according to the present invention.

FIG. 5 is a schematic diagram of a database of the present invention on a near-end computer.

FIG. 6 is a schematic diagram of a database placed on a remote server according to the present invention.

FIG. 7 is a schematic diagram of the first display interface of the present invention.

FIG. 8 is a schematic diagram of a movie according to the present invention.

FIG. 9 is a schematic diagram of the second display interface of the present invention.

FIG. 10 is a schematic diagram of the third display interface of the present invention.

FIG. 11 is a schematic diagram of a related object becoming a new main object according to the present invention.

FIG. 12 is a schematic flow chart of each display interface conversion according to the present invention.

13 is a schematic diagram of a first embodiment of a fourth display interface according to the present invention.

FIG. 14 is a schematic diagram of a second embodiment of the fourth display interface of the present invention.

FIG. 15 is a schematic diagram of a fifth display interface of the present invention.

In order to make your reviewing committee better understand the technical content of the present invention, specific preferred embodiments are described below.

Please refer to FIG. 1 for a hardware architecture diagram of the present invention. In this embodiment, the computer 1 includes a near-end computer 10 and a remote server 20. The near-end computer 10 in this embodiment is, for example, a tablet or a smart phone that is currently popular. The near-end computer 10 includes a processor 11, a memory 12, a screen 13, a speaker 14, an input device 15 (such as a microphone 15m), and a communication device 16 (such as wifi). The input device 15 is a microphone, or a touch module, a mouse, or a keyboard combined with the screen 13, and the memory 12 stores the language learning software program 30 (such as App) of the present invention.

The remote server 20 includes a processor 21, a memory 22 and a communication device 26. The memory 22 stores a language learning software program 30s. The remote server 20 is connected to the communication device 16 of the near-end computer 10 through the communication device 26 and the network 95.

In this embodiment, the computer 1 includes a structure of a near-end computer 10 and a remote server 20. The remote server 20 basically serves a plurality of near-end computers 10, and the near-end computer 10 and the remote server 20 The work can be designed in different ways, for example, almost all operations are performed on the remote server 20 or are concentrated on the near-end computer 10. For example, the remote server 20 is only responsible for updating the database of the near-end computer 10, member registration, or collecting the use behavior of the near-end computer 10. Other methods of language learning of the present invention are completed on the near-end computer 10, and the database is near It is best to have both the remote computer 10 and the remote server 20, but in general, the database of the remote server 20 is usually complete and complex. For example, the database of the remote server 20 includes the management of the use of different near-end computers 10. Or, there are different fields for artificial intelligence calculations, or collecting user data. It should be noted that the definition of the computer 1 in this specification may also include only the near-end computer 10 without the remote server 20, and the near-end computer 10 completely executes all the calculation processes of the present invention; the definition of computer 1 in this specification is also It may include only the remote server 20, and the remote server 20 executes all the calculation processes of the present invention and is used interactively via the near-end computer 10. Therefore, the language learning method of the present invention is completed by executing a language learning software program 30 (also including a language learning software program 30s) through the processor 11 (which may also include the processor 21). With regard to the near-end computer 10 and the remote server 20 Because the structure is a conventional technology, it will not be repeated here.

Please refer to FIG. 2 for a flowchart of the present invention, and refer to FIG. 3 to FIG. 13 together. For the convenience of clear description, the left block in FIG. 2 is the processing flow, and the right block is the output flow.

Step 201:

A first main object 41 is selected from a database 31 or a database 31s.

Step 202:

Select at least one first related object 43 from the database 31 or the database 31s. Please refer to Figure 5 ~ Figure 7. In this embodiment, the database 31 is placed in the memory 12 of the near-end computer 10, and the database 31s is placed in the memory 22 of the remote server 20. Generally, the data stored in the database 31s is relatively complete . It should be noted that the database can only be placed on the local computer 10 or the remote server 20, for example, when the computer 1 does not include the remote server 20, there will be no database 31s, but the data of the local computer 10 The database can use a relatively complete database such as the database 31s. This resource allocation may have different methods for convenient management. This is a well-known technology and will not be described here. The database 31 in this embodiment has fields such as text 31a, related words 31b, sentences 31c, degree 31d, number of learning 31e, voice file 31f, picture file 31g, movie file 31h, and movie label 31i. Because FIG. 5 cannot fit all the fields, FIG. 5 mainly shows the schematic diagrams of the fields 31a to 31i. The database 31s in this embodiment includes a text 31a, a related word 31b, a sentence 31c, a degree 31d, a learning number 31e, a weight 31j, and an association 31k. Among them, the weight 31j is the frequency in the database 31s by calculating the plural characters 31a and the plural related words 31b, and the highest frequency is set to the maximum weight, and the text with the maximum weight sentence is used as the first Related objects 43. Among them, association 31k is the logical relationship between the recorded text 31a and related words 31b in the sentence 31c of the database 31s, including: Relate (Issue A), Property (Property), Location (Location), Purpose, etc. Because Fig. 6 cannot fit all the fields, the fields 31f ~ 31i are not shown in Fig. 6, and Fig. 6 mainly shows the schematic diagrams of the fields 31a ~ 31e and 31j and 31k. Figures 5 and 6 only show a very small part of the database 31 or database 31s, and this database 31 or database 31s is only an embodiment. The way of storing fields in the database 31 or database 31s may be Many architectures.

Step 203:

A first main object 41 and at least one first related object 43 are displayed on a first display interface 131. Please refer to FIG. 7. In this embodiment, the first main object 41 is “Apple” and the first related object 43 is six, which are “Fruit”, “Tree”, “Banana”, “Eat”, “ House "," Apple Computer ". The "object" mentioned in this manual may include one of "image" or "text", or "image" and "text". In terms of Fig. 7, "object" includes "image" and "text". For example, the first main object 41 includes the first main text 41t and the corresponding first main image 41g, and each first related object 43 includes the first A related word 43t and a first related image 43g.

Each text has a corresponding voice file 31f, picture file 31g, and video file 31h, so that after the picture of the object, or voice, video, etc. can be displayed, data can be found. Of course, if the voice is software-to-speech, a voice file may not be needed. In addition, when the first main object 41 appears, the first main character 41t is sounded through the speaker 14 in a voice manner.

The first display interface 131 places the first main object 41 in the center, and six first related objects 43 surround the first main object 41, so that the user 90 (particularly suitable for children or English beginners) understands the subject It is to understand the first main object 41 (Apple), and the words related to the theme are the six first related objects 43 surrounding it.

There are many ways to select the first main object 41 in step 201, for example, in order or random, or the user 90 specifies the first main object 41 through the input device 15. The input device 15 may be a keyboard, a microphone, or a touch screen. Modules, for example, the user 90 says "Apple" through the microphone, so that the language learning software program 30 can select the first main object 41 from the database 31 and so on.

The selection of the first related object 43 in step 202 can also be specified by the user 90 in various ways. Another way is to select the first related object 43 related to the first main object 41 from the database 31. For example, "Apple" is selected as the first main object 41 as an example. Please refer to Fig. 5. "Apple" corresponds to many related words 31b, such as "Fruit" and "Tree", so the first related object 43 can be related from Pick among words 31b. For example, there are 200 related words 31b corresponding to "Apple". The method of selecting six related words 31b can be random, or based on the field of "degree 31d", "learning times 31e" or "weight 31j" reference.

Each "sentence 31c" has a corresponding record of "weight 31j", "associated 31k", "degree 31d", and "learning times 31e". The words recorded in the "sentence 31c" field contain the text 31a ( The first main text 41t, such as Apple), and related words 31b, such as "Apple is a fruit" have "Apple" and "fruit", and the 31k record relates to the relationship between "Apple" and "fruit": " Is-A ". If "weight 31j" is used as the basis for selecting "related words 31b", for example, the higher "weight 31j" corresponding to "word 31c", the more relevant it is to "Apple". For example, the weight score of "Apple is a fruit." Is "8", which means that the sentence "Apple is a fruit." Has a high probability of being used. Therefore, it also indirectly indicates that "fruit" and "Apple" are highly related. Therefore, picking out "fruit" as the first related object 43 should have priority. The score of "weight 31j" can actually be calculated through big data analysis. The easier the sentence to be used, the higher the score of "weight 31j".

The meaning of "degree 31d" represents the difficulty of "sentence 31c". For example, the longer the sentence "degree 31d", the harder it is, or the higher the difficulty of "word 31a", it will also affect "degree 31d". And "learning times 31e" is the number of times each "sentence 31c" is displayed or learned. Therefore, the first related object 43 can also refer to "degree 31d" or "learning times 31e" as an option. For example, a beginner can consider "degree 31d" or "learning times 31e" is too much, and it will no longer be the first Related object 43, for example, "Apple is a fruit." The number of learning times has reached 3 times, then "fruit" is no longer selected as the first related object 43.

Step 204:

Receiving a first selection instruction 71 forms a first target related object 43a. For example, the user 90 looks at the first display interface 131 displayed on the touch screen 13 (assuming that the near-end computer 10 is a lithographic version), and clicks "Fruit" the first related object 43, "Fruit" the first related object by hand 43 is the first target related object 43a. That is, when the user 90 is learning "Apple", he wants to know the relationship between "Apple" and "Fruit". In addition, when the first related object 43 is selected, the first related word 43t can also be voiced out through the speaker 14. An important feature of the present invention is that when learning a word (such as Apple), including understanding Apple through other words (such as Fruit). For example, if the user clicks "Tree" at 90, he wants to understand the relationship between "Apple" and "Tree". This kind of understanding of a word through different words can generate multiple angles or multiple levels to understand a single word, and it can make it more understandable. The user 90 thus learns about other words and sentences.

Step 205:

A first combined word 45 is selected from the database 31 or the database 31s. The first combined word 45 includes the first main character 41t and the first related word 43t of the first target related object 43a. For example, "Fruit" becomes the first target related object 43a, and the related phrases 31c of "Apple" and "Fruit" are "Apple is a fruit." And "Apple is a kind of fruit." Select at least one sentence, which can be selected randomly or according to the weight 31j, the degree 31d, or the number of learning 31e, or a plural number sentence. For example, the selection is based on a higher weight, and the first combination of words 45 is "Apple is a fruit."

Step 206:

A first main object 41 (Apple), a first target related object 43a (fruit), and a first combined word 45 ("Apple is a fruit.") Are displayed on a second display interface 132. Please refer to FIG. 9. The second display interface 132 allows the user 90 to learn between the first main object 41 (Apple) and the first target related object 43a (fruit) through the first combination 45 (“Apple is a fruit.”). Association. In a preferred embodiment, in addition to displaying the first combination of words 45 (“Apple is a fruit.”), The first combination of words 45 is also voiced through the speaker 14. Of course, the first combination of words 45 can also be sounded through the speaker 14 without displaying the first combination of words 45. It should be noted that in the embodiment, the first main object 41 (Apple) and the first target related object 43a (fruit) are both displayed, but they may not be displayed.

In addition to displaying the first combined sentence 45, the first combined alternative sentence 45a may also be displayed. The first combined substitution sentence 45a includes the first character 41t (Apple) in addition to the corresponding character 43t of the first target related object 43a. The first combined substitution sentence 45a is "Banana is a fruit."", The first combination of sentence 45 and the first combination of alternative sentence 45a have a common relationship and sentence pattern, so that children can understand the meaning of " is a " or be familiar with the composition of the sentence. Or replace the first main character 41t (Apple) with other related words (Apple Computer), the second combined substitution sentence 45b is "Apple computer is great.", The first combined sentence 45 and the second combined substitution sentence 45b Have the same connection and weight, so that children can understand (Apple) can represent objects with different meanings.

Step 207:

Receive a second selection command 72 (for the first main object 41), please refer to FIG. 9, if the user 90 continues to select the first main object 41 (such as Apple), for example, by clicking the touch screen 13 or by voice Say "Apple" through the microphone 15m to choose. This will return to the first display interface 131 (FIG. 7).

Step 208:

Receive a third selection instruction 73 (for the first target related object 43a) to form a second main object 47, please refer to FIG. 9, if the first target related object 43a (such as Fruit) is selected, the first target related object 43a becomes the new master: the second master 47.

Step 209:

At least one second related object 49 is selected from the database 31. Step 209 actually has the same meaning as step 202. When a new main object comes out, the corresponding related object must be selected for the new main object. Since the second main object 47 is "Fruit", the related object of "Fruit" must be selected. In this embodiment, five second related objects 49 are selected, such as "Table", "Egg", "Mango", "Apple", "Banana". For a description of step 209, please refer to step 202.

Step 210:

The third display interface 133 displays a second main object 47 and at least one second related object 49. Similarly, the second main text 47t and the second main image 47g of the second main object 47 are displayed, and the second related words 49t and the second related image 49g of each second related object 49 are also displayed. Step 210 is actually the same as the process of step 201.

According to the process explained above, FIG. 11 shows one of the important features of the present invention. The central circle represents the main object and the surrounding circles represent the related objects. Each connected object can form a new main object and develop a new related object. For example, in the group A, because the related object is clicked (step 204), after learning the combined sentence, clicking the related object can form a new main object, so for example, A1, A2, A3 can be developed. For example, the group A3, because the related objects were clicked, A31 and A32 were developed. And A32 can develop A321. In this way, new main objects and new related objects are constantly generated, and users can gradually learn more words and words according to their interests. Moreover, the related objects are gradually extended to the outside, that is, each user can follow his own Interest, one's own degree, one's own pace to learn, the present invention is suitable for various users through the generation of dynamic content.

In order to increase children's language learning fun, the above process can be matched with videos. Please refer to FIG. 12 for a schematic flowchart of the transition of each display interface after cooperating with the video, and also refer to FIG. 13 and FIG. 14 together.

Step S11:

A movie 80 can be played at the beginning.

Step S12:

When or after the video 80 is played, the fourth display interface 134 may display a plurality of candidate main objects 50 and the candidate main object words 50t. The user 90 selects the object that he likes to learn, for example, the user 90 selects the object "Apple", so the language learning software program 30 / 30s accepts the fourth selection instruction 74.

There are many ways to select a plurality of candidate main objects 50. For example, referring to FIG. 5, each text has a corresponding video, which is recorded in the video file 31h, and each video can record a video label 31i. For example, FIG. 13 is based on "Apple "Corresponding to the video label 31i, the six candidate main objects 50 of Apple, Fruit, House, Table, Banana, and Apple Computer are displayed. Of course, it is also possible to automatically obtain candidate main objects 50 and words 50t by analyzing the subtitles or movie images of the movie. Basically, the candidate main object 50, the word 50t is best to appear in the video, so that after the child has watched the video, the fourth display interface 134 displays these candidate main objects 50. The word 50t allows the child to choose when he just saw the video Interesting objects, or objects that you do n’t understand, can better enhance the learning effect of the video.

Step S13:

At this time, it switches to the first display interface 131. As shown in FIG. 7, "Apple" becomes the first main object 41.

Step S14:

After the user selects the related object, he can enter the second display interface 132. The second display interface 132 is mainly for learning the relationship between the main object and the related object. The second display interface 132 can also return to the first display interface 131 / the third display interface 133. For the description of steps S13 and S14, please refer to the flow description in FIG. 2 described above.

Step S15:

If the user clicks a video to play on the first display interface 131 (as shown in FIG. 7), the video 41v is played.

Step S16:

After the movie 41v is played, the fourth display interface 134 is displayed. Please refer to FIG. 14. Please also refer to the description of step S12 for this part. The fourth display interface 134 displays a plurality of candidate main objects 50 and words 50t after the video is played. Please refer to FIG. 14, the user 90 selects the object he likes to learn, for example, the user 90 selects the object “Fruit”, so the language learning software program 30 / 30s accepts the fourth selection instruction 74, and then enters the third display interface 133 As shown in FIG. 10, “Fruit” becomes the second main object 47.

The above steps S11 and S12 of FIG. 12 are only the first steps, so they are indicated by dashed squares, and then steps S13 to S16 are schematic diagrams of the interface conversion after the user starts learning.

Please refer to FIG. 3 together to show the manner in which the above-mentioned objects (such as the first main object 41, the second related object 49, and so on) are generated, and is not limited to one method, for example, after inputting by voice, then voice-to-text Either input via keyboard / mouse; or touch input with touch screen; or select by software with database, as explained in the above steps; or analyze video subtitles, voice or Image, or check the field of video tag 31i in the database.

According to the present invention, a new main object and a new related object are generated according to FIG. 11. The user gradually learns more words and words, and also selects single-layer or multi-layer main objects and related objects selected by the instruction 71 to 75. All are recorded, providing learners with repeated practice content, and these content can be compared with users for text, image, and voice comparison interactive quizzes. Please refer to FIG. 15 together, and the upper left is the comparison target image 51. The user is asked to select one of the four candidate images 51b, 51c, 51d, and 51e as the answer 51a.

Please refer to FIG. 4 together. The selection commands 71 to 75 can also be generated in various ways. For example, the user 90 can use voice mode, keyboard / mouse, or use touch mode input with the touch screen. It can be generated after 15m through the microphone. Select commands 71 ~ 75.

It should be noted that the above-mentioned many embodiments are examples for convenience of explanation, and the scope of the claimed rights of the present invention shall be based on the scope of the patent application, rather than being limited to the above-mentioned embodiments.

Claims (11)

    A language learning method is used on a computer and used by a user 90. The computer 10 includes a processor 11, a memory 12, a screen 13 and a speaker 14. The memory 12 includes a memory The language learning software program 30 includes a database 31, and the method executes the language learning software program 30 through the processor 11 to achieve the following steps: selecting a first main object 41 from the database 31, The database 31 includes a first main text 41t and a first main image 41g corresponding to the first main object 41, and the first main image 41g is a representative image of the first main text 41t. The first main text 41t or the first main image 41g corresponding to the first main object 41 is on the screen 13; at least one first related object 43 is selected from the database 31, wherein the database 31 includes the at least one The first related object 43 corresponds to a first related word 43t and a first related image 43g, and the first related image 43g is a representative image of the at least one first related word 43t; displaying the at least one The first related word corresponding to the first related object 43 43t or the first related image 43g on the screen 13; selecting a first combined word 45 from the database 31, the first combined word 45 includes the first main character 41t and the first related word 43t; And displaying the first combination of words 45 on the screen 13 or sounding the first combination of words 45 through the speaker 14.         The method for language learning as described in item 1 of the scope of patent application, wherein the at least one first related object 43 is a plurality of first related objects 43, and further includes receiving a first selection instruction 71, the first selection instruction is The user 90 selects one of the plurality of first related objects 43 to be the basis for selecting the first combination of words 45, and the selected first related object 43 forms a first target related object 43a.         The language learning method described in item 2 of the scope of the patent application, wherein the first main object 41 and the plurality of first related objects 43 are displayed on the screen 13 on a first display interface 131 and the display is displayed. The first combination of words 45 is displayed on the screen 13 on a second display interface 132, wherein the second display interface 132 further includes displaying the first main object 41 and the first target related object 43a, and further includes receiving a A third selection instruction 73, the third selection instruction is that the user 90 selects the first target related object 43a by the second display interface 132, so that the second display interface 132 converts to display a third display interface 133, wherein : The first target related object 43a forms a second main object 47, and the third display interface 133 displays the second main object 47; at least one second related object 49 is selected from the database 31, where the database 31 includes The at least second related object 49 corresponds to a second related word 49t and a second related image 49g, and the second related image 49g is a representative image of the at least one second related word 49t; the first Triple display interface 133 Shows at least one second connection of the object 49 corresponding to the second word 49t connected to the second related image or 49g.         The method for language learning according to item 3 of the scope of patent application, further comprising receiving a second selection instruction 72, the second selection instruction being selected by the user 90 and the second display interface 132 by the first main object 41, so that the second display interface 132 changes to display the first display interface 131.         The language learning method described in item 4 of the scope of the patent application, wherein the second display interface 132 further displays a first combination alternative sentence 45a, and the first combination alternative sentence 45a includes a corresponding one of the first target related object 43a. Text, the first combined sentence 45 and the first combined substitute sentence 45a have a common association and sentence pattern.         The language learning method described in item 5 of the scope of the patent application, wherein the second display interface 132 further displays a second combined substitute sentence 45b, and the second combined substitute sentence 45b includes a corresponding first of the first main character 41t. The related word 43t, the first combined sentence 45 and the second combined substitute sentence 45b have a common association and weight.         The language learning method as described in item 2 of the scope of the patent application, wherein the selection of the first main object 41 is an analysis of the voice generated by the user, the plurality of first related objects 43 or the first selection instruction 71 It is generated by analyzing the voice of the user.         The language learning method described in item 2 of the scope of patent application, wherein the first main object 41 is generated based on a film 80, and a plurality of candidate main objects 50 are generated according to the film 80, and a fourth display interface is used. 134 displays the candidate master object 50, and the first master object 41 is selected by receiving a fourth selection instruction 74 of one of the users, wherein the plurality of candidate master objects 50 are generated by analyzing the subtitles of the film 80. The voice of the movie 80 is generated according to the plural movie tags recorded in the movie 80; it also includes playing the movie 41v, and after the movie 41v is played, a plurality of candidate main objects 50 are generated according to the movie 80 A fourth display interface 134 displays the plurality of candidate main objects 50, and a second main object 47 is selected by receiving a fifth selection instruction 75 of one of the users.         According to the language learning method described in item 1 of the patent application scope, wherein the database 31 has a corresponding weight for recording the first combination of words 45, so that the weight will be referred to when the first combination of words 45 is selected.         The language learning method described in item 2 of the scope of patent application, which further includes using different classifications, selecting one or plural main objects 41, selecting one or plural related objects 43, selecting one or plural combined sentences 45, etc. to form different Level 90 user learning content on different topics.         The language learning method described in item 1 of the scope of patent application, which further includes recording the selected single-layer or multi-layer main object 41, related objects 43, and combination words 45 to form different networks, providing learners to repeat The content of the exercise; and a fifth display interface 135 displays the comparison target image 51, and the answer is selected from a plurality of candidate images 51b, 51c, 51d, 51e by receiving a sixth selection instruction 76 of one of the users One is as the corresponding image 51a, and the text, image, or voice comparison interactive question and answer is performed.