Text editing method and device, computer equipment and storage medium

Abstract

The invention discloses a text editing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining text content inputted by a target user through a target text field; identifying and analyzing context information of the text content, and extracting text features; based on the text features, determining a text template matched with the text content and recommendation information; and adding the text content into the text template, and continuing to write the text content through the recommendation information. In the embodiment of the invention, by identifying the text content and analyzing the context information of the text content, template setting and content recommendation can be carried out under the support of the internal knowledge base, and text continuous writing can be realized. Through the use of the knowledge base of the specific scene, the searched template and content are more accurate, and a more intelligent, efficient and personalized text input environment can be provided for the user.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a text editing method, apparatus, computer equipment and storage medium. Background Art

[0002] Currently, traditional text fields only provide basic character and text input functions, such as text, emoticons, links, etc. However, these text fields usually lack intelligent auxiliary functions and lack the support of proprietary internal knowledge bases. These limitations lead to low efficiency in the process of users inputting content in text fields and difficulty in ensuring text quality, especially in the context of a large number of users and growing personalized needs. Summary of the invention

[0003] Based on this, it is necessary to provide a text editing method, apparatus, computer device and storage medium to address the above technical issues, so as to solve at least one problem existing in the above-mentioned prior art.

[0004] In a first aspect, the embodiment of the present application is implemented by providing a text editing method, the method comprising:

[0005] Get the text content entered by the target user through the target text field;

[0006] Identify and analyze the context information of the text content and extract text features;

[0007] Based on the text features, determining a text template and recommended information that matches the text content;

[0008] The text content is added to the text template, and the text content is continued using the recommendation information.

[0009] In one embodiment, determining a text template and recommendation information matching the text content based on the text feature includes:

[0010] Determining attribute information of the target text field and user login information of the target user;

[0011] Determining a feature tag of the target user based on the attribute information of the target text field and the user login information of the target user;

[0012] Based on the feature tags and the text features, a text template and recommended information matching the text content are searched from a knowledge base.

[0013] In one embodiment, after obtaining the text content input by the target user through the target text field, the method includes:

[0014] Performing consistency check on the text content;

[0015] Performing content integrity detection on the text content;

[0016] When the text content has inconsistency problems and / or incomplete problems, the text content is corrected and optimized based on the detection results.

[0017] In one embodiment, the consistency check includes at least one of a placeholder consistency check, a semantic consistency check, a text style check, and a text format check.

[0018] In one embodiment, the consistency detection of the text content includes:

[0019] Performing word segmentation processing on the text content to obtain multiple words;

[0020] Determine the word vector corresponding to each of the words;

[0021] Based on the similarities between the different word vectors, determining whether there is a target word with a similarity greater than a first preset threshold and less than a second preset threshold;

[0022] If so, output prompt information to unify the target words.

[0023] In one embodiment, after obtaining the text content input by the target user through the target text field, the method includes:

[0024] Inputting the text content into a semantic analysis model for processing to obtain a semantic feature vector;

[0025] Extracting keywords from the text content;

[0026] Based on the keywords, determining a usage scenario corresponding to the text content;

[0027] Based on the usage scenario and the semantic feature vector, the format and style of the text content are automatically adjusted.

[0028] In one embodiment, after obtaining the text content input by the target user through the target text field, the method includes:

[0029] Determine the task scenario corresponding to the text content;

[0030] Based on the task scenario, determining whether the text content contains task rule information;

[0031] If not, a prompt message is output to prompt the target user to add the task rule information.

[0032] In a second aspect, a text editing device is provided, comprising:

[0033] A text content acquisition unit, used to acquire the text content input by the target user through the target text field;

[0034] A text feature extraction unit, used to identify and analyze the context information of the text content and extract text features;

[0035] An associated information determination unit, configured to determine a text template and recommended information matching the text content based on the text feature;

[0036] A processing unit is used to add the text content to the text template and continue writing the text content according to the recommendation information.

[0037] In a third aspect, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the text editing method as described above when executing the computer-readable instructions.

[0038] In a fourth aspect, a readable storage medium is provided, wherein the readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the text editing method as described above are implemented.

[0039] The above-mentioned text editing method, device, computer equipment and storage medium, and its method implementation include: obtaining the text content input by the target user through the target text field; identifying and analyzing the context information of the text content, extracting text features; based on the text features, determining the text template and recommendation information that matches the text content; adding the text content to the text template, and continuing the text content through the recommendation information. In the embodiment of the present application, by identifying the text content and analyzing its context information, template setting and content recommendation can be performed with the support of the internal knowledge base, and text continuation can be achieved. Through the use of the knowledge base of specific scenarios, the templates and content found are more accurate, which can provide users with a more intelligent, efficient and personalized text input environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1is a schematic diagram of an application environment of a text editing method in an embodiment of the present application;

[0042] Figure 2 is a flowchart of a text editing method in an embodiment of the present application;

[0043] Figure 3 is a structural schematic diagram of a text editing device in one embodiment of the present application;

[0044] Figure 4 It is a schematic diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The text editing method provided in this embodiment can be applied to Figure 1 In the application environment, the text editor provides a text field for users to input text content. The text field AI component of the text editor can communicate with the big model, and the big model can be used to analyze and contextualize the text content through AI, and query the pre-built knowledge base based on text features to achieve the continuation, optimization and correction of the text content, and output the processed plan. At the same time, the continued content can be corrected and optimized again to obtain high-quality text that meets user expectations.

[0047] Among them, the large model can be configured on the freight platform. When the user enters text content through the text field, such as asking questions, the text field AI component can pass parameters to call the large model in the freight platform to generate and call the answer text, and feedback to the user.

[0048] In one embodiment, if Figure 2 As shown, a text editing method is provided, comprising the following steps:

[0049] In step S110, the text content input by the target user through the target text field is obtained;

[0050] In the embodiment of the present application, the target text field may be a text field provided by a text editor, which may specifically include a text input area, and a user may input text for editing through a graphical user interface GUI provided by the text editor. The text field also provides AI auxiliary functions, such as intelligent continuation, intelligent questioning, intelligent translation, etc., to provide corresponding auxiliary functions according to user needs.

[0051] In an embodiment of the present application, when a user is detected performing a text content input operation, the text content can be identified and analyzed to determine whether the text content is valid, for example, whether it is garbled code input by accident, or semantically incomprehensible content. If so, the entered text content is automatically deleted and the target text field interface is closed.

[0052] In the embodiment of the present application, the text content may include a word, a sentence, a paragraph or a combination of multiple words, etc. The user may directly input the text, or input the text content by voice, automatic conversion to text or copying the text content.

[0053] In step S120, the context information of the text content is identified and analyzed to extract text features;

[0054] In an embodiment of the present application, the text content can be context-aware, for example, time information, task scenarios, usage scenarios, etc., to determine the context information, and extract text features of the text content based on the context information. The text features may specifically include scenes, user roles, locations, events, time, etc.

[0055] In step S130, based on the text features, a text template and recommendation information matching the text content are determined;

[0056] In an embodiment of the present application, the text editor can be connected to a large model, and the large model is connected to an internal knowledge base, which can provide knowledge about the freight industry and real business knowledge. For example, it can be connected to a freight event center system. The knowledge base may include templates and text information for different scenarios and different businesses, as well as user historical usage records. And multiple knowledge sub-bases can be established in the knowledge base, that is, a corresponding knowledge sub-base is set for different fields, different scenarios, and different businesses, so that when calling, the corresponding knowledge sub-base can be used directly, without the need to check all templates and information, reducing search time and improving search efficiency.

[0057] The text template can be set for different scenarios and different businesses, including text templates for questions, notices, rule making, etc. The recommended content can be the corresponding professional knowledge content for different scenarios and different businesses. For example, if the user needs a service description for moving services, the service content related to moving services can be called up and integrated, and invalid content, such as expired and duplicate content, can be deleted to obtain the final recommended information.

[0058] In step S140, the text content is added to the text template, and the text content is continued using the recommendation information.

[0059] In an embodiment of the present application, after obtaining the text content, keywords of the text content, such as scene, role, place, time, symbol and other information, can be extracted, and the text content can be semantically analyzed to determine its meaning, thereby determining to find a template corresponding to the text. For example, if it is a notification letter, the text content can be continued based on the recommended information obtained to generate a complete notification letter, thereby reducing the user's manual input time during the text input process and improving text generation efficiency.

[0060] As an implementation scenario, users only need to enter a few keywords, and the model will analyze them, then directly generate the continuation of related content based on the keywords, and generate the complete text according to the template. For example, users only need to enter the event, time, place, and object, and the model can automatically search for the corresponding recommended content and continue writing.

[0061] It should be noted that after obtaining the text content, it is also possible to determine whether the text content is a question text. If it is a question text, a template related to the question is obtained, and the answer text information corresponding to the question is obtained, entered into the template as recommended information, and presented to the user to answer the user's question.

[0062] In the embodiment of the present application, when the continuation is completed, a complete text can be obtained, and then the AI ​​model can be used to check the content consistency and integrity of the complete text, and to check whether there are typos, symbol errors, etc. At the same time, the text format, style, etc. can be adjusted according to the context to generate high-quality, directly usable text without the need for manual inspection and modification by the user.

[0063] In an embodiment of the present application, a text editing method is provided, including: obtaining text content input by a target user through a target text field; identifying and analyzing the context information of the text content, extracting text features; based on the text features, determining a text template and recommendation information that matches the text content; adding the text content to the text template, and continuing the text content through the recommendation information. In an embodiment of the present application, by identifying the text content and analyzing its context information, template setting and content recommendation can be performed with the support of an internal knowledge base, and text continuation can be achieved, reducing the manual input and proofreading time of users during the text input process, improving editing efficiency, and providing an automatic correction function to reduce grammatical and spelling errors in the text, improve text professionalism and readability, so that users can enjoy a more intelligent, efficient, and personalized text input environment, while ensuring text quality and consistency.

[0064] In an embodiment of the present application, the determining of a text template and recommendation information matching the text content based on the text feature includes:

[0065] Determine the attribute information of the target text field and the user login information of the target user,

[0066] Determining a feature tag of the target user based on the attribute information of the target text field and the user login information of the target user;

[0067] Based on the feature tags and the text features, a text template and recommended information matching the text content are searched from a knowledge base.

[0068] The attribute information of the target text domain may specifically include the module to which the target text domain currently belongs, such as different modules such as moving service, car rental service, logistics service, etc. The user login information may specifically include user account, name, contact information, role, such as staff, consumer, manager, etc.

[0069] Optionally, by obtaining the attribute information and the user login information, it can be determined whether the user is a new user, a consumer or a staff member, whether the user frequently uses the service or is using the service for the first time, etc., and then a corresponding feature tag can be added. The feature tag may include at least one. Then, based on the feature tag and the text feature, a template of the service frequently used by the user under the module to which the target text domain currently belongs can be selected from a pre-built knowledge base, and content related to the text content can be determined based on the text feature, and the text content input by the user can be continued using the content to generate a complete text, which is then presented to the user.

[0070] In an embodiment of the present application, after obtaining the text content input by the target user through the target text field, the method includes:

[0071] Performing consistency check on the text content;

[0072] Performing content integrity detection on the text content;

[0073] When the text content has inconsistency problems and / or incomplete problems, the text content is corrected and optimized based on the detection results.

[0074] Optionally, in order to ensure the quality and consistency of the text, the text can be checked for consistency so that the terms, formats and styles used in the text can be kept consistent. For example, if "moving service" and "handling service" appear in the text, according to the semantics, the two have the same meaning. Therefore, the two words can be highlighted, such as marked in red, and then a prompt message can be output to prompt the user to unify the terms. Or when there are two fonts, Kaiti and Songti, in the text, a prompt message can also be output to prompt the user to unify the font. This makes the words, fonts, and formats used before and after the text consistent, so as to improve the quality and readability of the text.

[0075] The consistency check includes at least one of a placeholder consistency check, a semantic consistency check, a text style check, and a text format check.

[0076] At the same time, the integrity of the text can be checked to avoid missing key information. For example, if the text mentions "task completion time" but does not have a specific date, the user can be prompted to add the date. At the same time, typos and symbol errors can also be checked to avoid affecting the overall quality of the text.

[0077] In an embodiment of the present application, the consistency detection of the text content includes:

[0078] Performing word segmentation processing on the text content to obtain multiple words;

[0079] Determine the word vector corresponding to each of the words;

[0080] Based on the similarities between the different word vectors, determining whether there is a target word with a similarity greater than a first preset threshold and less than a second preset threshold;

[0081] If so, output prompt information to unify the target words.

[0082] Optionally, the input text content can be segmented, and then the joint distribution probabilities corresponding to different segmentation methods are counted through the established corpus, and the segmentation method corresponding to the maximum probability is found to obtain the optimal segmentation result. Then, based on the optimal segmentation result, multiple words are determined, and the word vector corresponding to each word can be obtained through one-hot encoding or word vector model. The similarity between each word vector can then be calculated by cosine similarity or Euclidean distance. If the similarity is greater than a preset threshold, but it is not completely similar, for example, the similarity is less than 100% and greater than 95%, it means that there may be inconsistencies between the two. At this time, the semantics of the two words can be further analyzed to determine whether they represent the same meaning. If so, a prompt message can be output to prompt the user to unify the word content so that words with the same semantics can be unified.

[0083] It should be noted that the number of times two words appear can be determined, and the word with fewer occurrences can be replaced with the word with more occurrences. For example, if moving service appears 5 times and transport service only appears once, it can be considered that there is a typo and transport service can be changed to moving service.

[0084] In an embodiment of the present application, after obtaining the text content input by the target user through the target text field, the method includes:

[0085] Inputting the text content into a semantic analysis model for processing to obtain a semantic feature vector to extract keywords from the text content;

[0086] Based on the keywords, determining a usage scenario corresponding to the text content;

[0087] Based on the usage scenario and the semantic feature vector, the format and style of the text content are automatically adjusted.

[0088] Among them, specific usage scenarios may include asking questions related to moving services, order inquiries under logistics services, etc.

[0089] Among them, the semantic analysis model can be a DSSN model, for example, CNN-DSSM, LSTM-DNSS, etc.

[0090] Optionally, the input text content is analyzed by a semantic analysis model to determine its semantic feature vector. Then, the format and style of the text content can be automatically adjusted based on the usage scenario and the semantic feature vector. For example, if the text content is a notice letter for a moving service, the header, footer and title of the text content are adjusted to the format of the notice letter, such as making the title font bold or changing to a larger font size, and adding a moving service logo.

[0091] In one embodiment of the present application, after obtaining the text content input by the target user through the target text field, the method includes:

[0092] Determine the task scenario corresponding to the text content;

[0093] Based on the task scenario, determining whether the text content contains task rule information;

[0094] If not, a prompt message is output to prompt the target user to add the task rule information.

[0095] Optionally, during the text editing process, the text editor may integrate checks and prompts with specific rules to ensure that the text meets specific requirements. For example, if the text is a task description for a moving service, the editor will check whether all necessary task rule information is included and provide prompts if it is missing. For example, prompts may be provided by outputting voice, pop-up windows, ringing bells, sounds and lights, or text.

[0096] In the embodiments of the present application, by identifying the text content and analyzing its contextual information, template setting and content recommendation can be performed with the support of the internal knowledge base, and text continuation can be achieved. The use of the knowledge base for specific scenarios makes the templates and content found more accurate, providing users with a more intelligent, efficient and personalized text input environment.

[0097] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] In one embodiment, a text editing device is provided, which corresponds one-to-one to the text editing method in the above embodiment. Figure 3 As shown, the text editing device includes a text content acquisition unit 10, a text feature extraction unit 20, a related information determination unit 30 and a processing unit 40. The functional modules are described in detail as follows:

[0099] The text content acquisition unit 10 is used to acquire the text content input by the target user through the target text field;

[0100] A text feature extraction unit 20, used to identify and analyze the context information of the text content and extract text features;

[0101] The associated information determining unit 30 is used to determine a text template and recommended information matching the text content based on the text feature;

[0102] The processing unit 40 is used to add the text content to the text template and continue writing the text content according to the recommendation information.

[0103] In one embodiment of the present application, the association information determination unit 30 is further configured to:

[0104] Determine the attribute information of the target text field and the user login information of the target user,

[0105] Determining a feature tag of the target user based on the attribute information of the target text field and the user login information of the target user;

[0106] Based on the feature tags and the text features, a text template and recommended information matching the text content are searched from a knowledge base.

[0107] In one embodiment of the present application, the device further includes a text correction unit, which is used to:

[0108] Performing consistency check on the text content;

[0109] Performing content integrity detection on the text content;

[0110] When the text content has inconsistency problems and / or incomplete problems, the text content is corrected and optimized based on the detection results.

[0111] In an embodiment of the present application, the consistency check includes at least one of a placeholder consistency check, a semantic consistency check, a text style check, and a text format check.

[0112] In one embodiment of the present application, the text correction unit is further used to:

[0113] Performing word segmentation processing on the text content to obtain multiple words;

[0114] Determine the word vector corresponding to each of the words;

[0115] Based on the similarities between the different word vectors, determining whether there is a target word with a similarity greater than a first preset threshold and less than a second preset threshold;

[0116] If so, output prompt information to unify the target words.

[0117] In one embodiment of the present application, the device further includes a text optimization unit, which is used to:

[0118] Inputting the text content into a semantic analysis model for processing to obtain a semantic feature vector to extract keywords from the text content;

[0119] Based on the keywords, determining a usage scenario corresponding to the text content;

[0120] Based on the usage scenario and the semantic feature vector, the format and style of the text content are automatically adjusted.

[0121] In one embodiment of the present application, the device further includes a prompting unit, which is used to:

[0122] Determine the task scenario corresponding to the text content;

[0123] Based on the task scenario, determining whether the text content contains task rule information;

[0124] If not, a prompt message is output to prompt the target user to add the task rule information.

[0125] In the embodiments of the present application, by identifying the text content and analyzing its contextual information, template setting and content recommendation can be performed with the support of the internal knowledge base, and text continuation can be achieved, reducing the user's manual input and proofreading time during the text input process, improving editing efficiency, and providing automatic correction function to reduce grammatical and spelling errors in the text, improve the professionalism and readability of the text, so that users can enjoy a more intelligent, efficient and personalized text input environment, while ensuring the quality and consistency of the text.

[0126] For the specific definition of the text editing device, please refer to the definition of the text editing method above, which will not be repeated here. Each module in the above-mentioned text editing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0127] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a text editing method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0128] In an embodiment of the present application, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the text editing method described above when executing the computer-readable instructions.

[0129] In an embodiment of the application, a readable storage medium is provided, wherein the readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the text editing method as described above are implemented.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0132] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A text editing method, characterized in that: The method comprises: Get the text content entered by the target user through the target text field; Identify and analyze the context information of the text content and extract text features; Based on the text features, determining a text template and recommended information that matches the text content; The text content is added to the text template, and the text content is continued using the recommendation information.

2. The text editing method according to claim 1, characterized in that: The determining, based on the text features, a text template and recommendation information matching the text content includes: Determining attribute information of the target text field and user login information of the target user; Determining a feature tag of the target user based on the attribute information of the target text field and the user login information of the target user; Based on the feature tags and the text features, a text template and recommended information matching the text content are searched from a knowledge base.

3. The text editing method according to claim 1, characterized in that: After obtaining the text content input by the target user through the target text field, the method includes: Performing consistency check on the text content; Performing content integrity detection on the text content; When the text content has inconsistency problems and / or incomplete problems, the text content is corrected and optimized based on the detection results.

4. The text editing method according to claim 3, characterized in that: The consistency check includes at least one of a placeholder consistency check, a semantic consistency check, a text style check, and a text format check.

5. The text editing method according to claim 3, characterized in that: The consistency detection of the text content includes: Performing word segmentation processing on the text content to obtain multiple words; Determine the word vector corresponding to each of the words; Based on the similarities between the different word vectors, determining whether there is a target word with a similarity greater than a first preset threshold and less than a second preset threshold; If so, output prompt information to unify the target words.

6. The text editing method according to claim 1, characterized in that: After obtaining the text content input by the target user through the target text field, the method includes: Inputting the text content into a semantic analysis model for processing to obtain a semantic feature vector; Extracting keywords from the text content; Based on the keywords, determining a usage scenario corresponding to the text content; Based on the usage scenario and the semantic feature vector, the format and style of the text content are automatically adjusted.

7. The text editing method according to claim 1, characterized in that: After obtaining the text content input by the target user through the target text field, the method includes: Determine the task scenario corresponding to the text content; Based on the task scenario, determining whether the text content contains task rule information; If not, a prompt message is output to prompt the target user to add the task rule information.

8. A text editing device, characterized in that: The device comprises: A text content acquisition unit, used to acquire the text content input by the target user through the target text field; A text feature extraction unit, used to identify and analyze the context information of the text content and extract text features; An associated information determination unit, configured to determine a text template and recommended information matching the text content based on the text feature; A processing unit is used to add the text content to the text template and continue writing the text content according to the recommendation information.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the text editing method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the text editing method according to any one of claims 1 to 7 are implemented.

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