Visual communication design and display system based on model analysis

Abstract

The invention discloses a visual communication design and display system based on model analysis, which relates to the technical field of visual communication design and comprises a design demand analysis unit, a design output unit, a design display unit, a user feedback analysis unit and a design optimization unit. According to the method, the natural language commands are recognized through the large language model, the design requirements expressed by the natural language commands are determined, the corresponding design system, the page image and the page template are determined based on the design requirements, the pilot-run user interface design draft is generated and then displayed to the user side, the user portrait is established based on the basic information of the user, and the user experience is improved. Meanwhile, a user behavior table is constructed based on behavior data, the user behavior table is analyzed through a deep learning model, and then the pilot run simulation interface is evaluated to obtain an evaluation result; and based on an evaluation result, performing user interface response type design optimization adapting to different user requirements on a page module corresponding to the pilot run simulation interface.

Description

Technical Field

[0001] The present invention relates to the technical field of visual communication design, and in particular to a visual communication design and display system based on model analysis. Background Art

[0002] Visual communication design is a form of design that uses visual elements (such as graphics, text, color, layout, etc.) to convey information, express ideas and emotions. It aims to enhance the readability, appeal and influence of information through visual means. Display system refers to the overall design scheme and technical system used to display information, products, artworks or brand images. It is not just the spatial layout of physical display, but also a combination of a set of comprehensive design, interactive technology, visual communication and other elements.

[0003] Visual communication design and display systems are closely related fields. The two work together to affect the way we obtain information, perceive the brand image, and experience art and culture. With the continuous development of technology and design concepts, visual communication design and display systems will increasingly rely on digital technology, interactive means and creative elements to provide a richer and more interactive experience. User interface design is an important application field of visual communication design, including the interface design of mobile applications, websites, and software, to ensure smooth user experience and convey correct information. User interface design is a very comprehensive job that needs to consider user needs, functional requirements, platform characteristics, visual design, interaction methods, accessibility and other aspects. Excellent user interface design must not only meet technical requirements, but also pay attention to user experience. However, user experience and user needs are multifaceted and it is difficult to meet the different needs of different users. Therefore, it is difficult for existing user interface designs to be comprehensively met based on actual feedback from user experience.

[0004] In view of the above-mentioned technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to evaluate the trial operation simulation interface based on user characteristic coefficients and behavior data to obtain an evaluation result, and based on the evaluation result, optimize the user interface responsive design of the page module corresponding to the trial operation simulation interface to adapt to different user needs.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: a visual communication design and display system based on model analysis, including a design demand analysis unit, a design output unit, a design display unit, a user feedback analysis unit and a design optimization unit; The design requirement analysis unit includes a model recognition module and a requirement analysis module, wherein the model recognition module is used to obtain natural language commands, and recognize each natural language command through a large language model, determine the design requirements expressed by each natural language command, and send them to the requirement analysis module, wherein the large language model is obtained by training based on structured knowledge in the field of user interface design; The requirement analysis module is used to obtain design requirements, and determine the corresponding design system, page image and page template based on the design requirements, use the resources of the design system to construct the pseudo code corresponding to the page template, and send the pseudo code to the design output unit; The design output unit is used to convert the pseudo code into a corresponding vector design draft, and determine the corresponding style and filling content based on the page image, fill the vector design draft with the style and the filling content, and output the obtained trial user interface design draft to the design display unit; The design display unit is used to obtain the user interface design draft of the trial run and the target user interface, and generate a trial run simulation interface based on the user interface design draft of the trial run, and display the trial run simulation interface to the user end, and generate an actual operation interface based on the target user interface, and display the actual operation interface to the user end; The user feedback analysis unit includes a user analysis module and a feedback analysis module. The user analysis module is used to obtain basic information of the user, establish a user portrait according to the basic information, and classify the user portrait according to the clustering model to obtain a user feature recognition result. The user feature coefficient is calculated based on the user feature recognition result and sent to the feedback analysis unit; The feedback analysis module is used to obtain the user's behavior data on the trial operation simulation interface, build a user behavior table based on the behavior data, analyze the user behavior table through a deep learning model to obtain user behavior analysis data, evaluate the trial operation simulation interface according to the user characteristic coefficient and the user behavior analysis data to obtain an evaluation result, and send it to the interface optimization unit; The design optimization unit is used to obtain and process the evaluation results, and based on the evaluation results, perform user interface responsive design optimization on the page modules corresponding to the trial operation simulation interface to adapt to different user needs, and obtain the target user interface and send it to the design display unit again.

[0007] Furthermore, the specific process of obtaining a large language model is as follows: S101, the natural language command includes user needs and goals, functional requirements, platform and device characteristics, visual design principles, interaction methods, accessibility, brand and visual identity, and technical feasibility; S102, constructing a knowledge graph based on data from different natural language command sources, using a depth-first search algorithm to obtain a language entity for each entity based on a search path corresponding to each entity in each knowledge graph, and performing cleaning and word segmentation on the language entity to obtain training data; S103, using a clustering algorithm to obtain the relevance of training data for each knowledge graph, and obtaining target task data corresponding to the training data; S104. Build a large language training model base based on the Transformer architecture, input the training data into the large language training model base for pre-training to obtain a pre-trained large language model, and fine-tune the pre-trained model using the target task data to obtain an optimized large language model.

[0008] Furthermore, the specific process of obtaining the user interface design draft for trial operation is as follows: S201, retrieving associated styles and filling contents from a network database based on the page image, and integrating the styles and filling contents into a candidate data set; S202, calculating the correlation coefficient between each style and filling content in the candidate data set and the design requirement according to the clustering algorithm, and expressing it through a specific numerical value, and marking the correlation coefficient at the subscript of the style and filling content; S203, rearrange the styles and filling contents in the candidate data sets according to the correlation coefficients from large to small, fill the several groups of styles and filling contents arranged in the front row into the vector design draft one by one, and obtain the pseudo user interface design draft; S204, inputting the proposed user interface design draft into the target model, obtaining the verification result output by the target model, and selecting the user interface design draft for trial operation based on the verification result; The target model is a neural network model obtained by iteratively training a number of normal user interface design drafts and a number of abnormal user interface design drafts, wherein the abnormal user interface design drafts are user interface components that do not conform to preset design rules.

[0009] Furthermore, the specific process of generating the trial run simulation interface is as follows: S301, obtaining interface configuration data corresponding to the user interface design draft, generating an initial user interface based on the interface configuration data, wherein the interface configuration data is used to represent the style, filling content and page template of the user interface design draft, and the initial user interface is displayed in the user terminal; S302: Configure the initial user interface to generate a trial operation simulation interface, where the trial operation simulation interface matches the user interface design draft.

[0010] Furthermore, the specific process of calculating the user characteristic coefficient is as follows: S401. Obtaining basic information of the user, wherein the basic information includes user personal information, historical behavior data, and historical social data. The user personal information includes the user's gender, age, and occupation. The historical behavior data includes historical browsing records, historical click records, historical search data, and historical purchase data. The historical social data includes the user's interactive behavior on the social platform, including the number of likes, comment content, and sharing data. S402, adding tags to the user's personal information, historical behavior data, and historical social data to obtain a user tag set, determining a feature vector set based on the user tag set, and constructing a user portrait based on the user tag set and the feature vector set; S403, constructing a user feature matrix based on the user's behavioral characteristics and consumption characteristics, and identifying the feature type of the user portrait through a clustering model, specifically hierarchical clustering; S404: extract the feature keywords of the feature type, identify the portrait feature factors in the feature type, and then obtain the user feature recognition result, and calculate the user feature coefficient R (G, F) according to the following formula: , where e1 and e2 are preset proportional coefficients, n represents the number of samples corresponding to the image feature factor, Gi is the i-th sample value of the parameter corresponding to other factors in the image feature factor, is the sample mean of the parameters corresponding to other factors in the image feature factor, Fi is the i-th sample value of the parameters corresponding to other factors in the image feature factor, It is the sample mean of the corresponding parameters of other factors in the portrait feature factor. The user feature coefficient is used to indicate the correlation between the user and the corresponding feature type. The larger the user feature coefficient is, the higher the correlation with the corresponding feature type is. Conversely, the smaller the user feature coefficient is, the lower the correlation with the corresponding feature type is.

[0011] Furthermore, the specific process of evaluating the trial operation simulation interface is as follows: S501, obtaining user behavior data on the trial operation simulation interface, wherein the behavior data includes a user ID and a corresponding user characteristic coefficient, a routed page, a stay time, a link jump number, and a function usage number; S502: construct a timeline based on the user's operation time, and mark the behavior data according to the corresponding time nodes on the timeline to obtain a user behavior table, analyze the user behavior table, including aggregation comparison and sorting, to obtain user behavior analysis data, the user behavior analysis data including user ID, number of clicks on high-frequency pages, number of clicks on high-frequency links, and number of clicks on high-frequency functions; S503: Obtain user feature coefficients, and calculate the correlation coefficients Ui between the portrait feature factors in the user feature coefficients and the user behavior analysis data one by one according to the following formula: , where d is the preset weight coefficient, N is the total number of user operations, and fi is the number of clicks on high-frequency pages or high-frequency links or high-frequency functions; S504, obtaining a preset association judgment threshold, if the association coefficient Ui is greater than or equal to the association judgment threshold, then the evaluation result of the trial operation simulation interface meets the user's needs; If the correlation coefficient Ui is less than the correlation judgment threshold, the evaluation result of the trial operation simulation interface is that it does not meet the user needs.

[0012] Furthermore, the specific operations for responsive design optimization of the trial run simulation interface are as follows: Adjusting the interface layout of the trial operation simulation interface; Adding new controls in the trial run simulation interface; Deleting existing controls from the trial run simulation interface; Adjusting the control properties of the controls in the trial operation simulation interface; Adjusting the control style of the control in the trial operation simulation interface; The link position of the control that needs to jump in the trial operation simulation interface is adjusted.

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This visual communication design and display system based on model analysis recognizes each natural language command through a large language model, determines the design requirements expressed by each natural language command, and determines the corresponding design system, page image and page template based on the design requirements. It generates a trial run user interface design draft and displays it to the user end. It establishes a user portrait based on the user's basic information and obtains the user feature coefficient. At the same time, it constructs a user behavior table based on the behavior data, analyzes the user behavior table through a deep learning model, and then evaluates the trial run simulation interface to obtain an evaluation result. Based on the evaluation result, the page module corresponding to the trial run simulation interface is optimized for user interface responsiveness to meet different user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The overall system structure schematic diagram of the present invention is shown. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0016] like Figure 1 As shown, the visual communication design and display system based on model analysis includes a design requirement analysis unit, a design output unit, a design display unit, a user feedback analysis unit and a design optimization unit; The design requirement analysis unit includes a model recognition module and a requirement analysis module, wherein the model recognition module is used to obtain natural language commands, and recognize each natural language command through a large language model, determine the design requirements expressed by each natural language command, and send them to the requirement analysis module, wherein the large language model is obtained by training based on structured knowledge in the field of user interface design; The specific process of obtaining a large language model is as follows: S101, the natural language command includes user needs and goals, functional requirements, platform and device characteristics, visual design principles, interaction methods, accessibility, brand and visual identity, and technical feasibility; S102, constructing a knowledge graph based on data from different natural language command sources, using a depth-first search algorithm to obtain a language entity for each entity based on a search path corresponding to each entity in each knowledge graph, and performing cleaning and word segmentation on the language entity to obtain training data; S103, using a clustering algorithm to obtain the relevance of training data for each knowledge graph, and obtaining target task data corresponding to the training data; S104. Build a large language training model base based on the Transformer architecture, input the training data into the large language training model base for pre-training to obtain a pre-trained large language model, and fine-tune the pre-trained model using the target task data to obtain an optimized large language model.

[0017] The requirement analysis module is used to obtain design requirements, and determine the corresponding design system, page image and page template based on the design requirements, use the resources of the design system to construct the pseudo code corresponding to the page template, and send the pseudo code to the design output unit; The design output unit is used to convert the pseudo code into a corresponding vector design draft, and determine the corresponding style and filling content based on the page image, fill the vector design draft with the style and the filling content, and output the obtained trial user interface design draft to the design display unit; The specific process of getting the user interface design draft for trial operation is as follows: S201, retrieving associated styles and filling contents from a network database based on the page image, and integrating the styles and filling contents into a candidate data set; S202, calculating the correlation coefficient between each style and filling content in the candidate data set and the design requirement according to the clustering algorithm, and expressing it through a specific numerical value, and marking the correlation coefficient at the subscript of the style and filling content; S203, rearrange the styles and filling contents in the candidate data sets according to the correlation coefficients from large to small, fill the several groups of styles and filling contents arranged in the front row into the vector design draft one by one, and obtain the pseudo user interface design draft; S204, inputting the proposed user interface design draft into the target model, obtaining the verification result output by the target model, and selecting the user interface design draft for trial operation based on the verification result; The target model is a neural network model obtained by iteratively training a number of normal user interface design drafts and a number of abnormal user interface design drafts, wherein the abnormal user interface design drafts are user interface components that do not conform to preset design rules.

[0018] The design display unit is used to obtain the user interface design draft of the trial run and the target user interface, and generate a trial run simulation interface based on the user interface design draft of the trial run, and display the trial run simulation interface to the user end, and generate an actual operation interface based on the target user interface, and display the actual operation interface to the user end; The specific process of generating the trial run simulation interface is as follows: S301, obtaining interface configuration data corresponding to the user interface design draft, generating an initial user interface based on the interface configuration data, wherein the interface configuration data is used to represent the style, filling content and page template of the user interface design draft, and the initial user interface is displayed in the user terminal; S302: Configure the initial user interface to generate a trial operation simulation interface, where the trial operation simulation interface matches the user interface design draft.

[0019] The user feedback analysis unit includes a user analysis module and a feedback analysis module. The user analysis module is used to obtain basic information of the user, establish a user portrait according to the basic information, and classify the user portrait according to the clustering model to obtain a user feature recognition result. The user feature coefficient is calculated based on the user feature recognition result and sent to the feedback analysis unit; The specific process of calculating the user characteristic coefficient is as follows: S401. Obtaining basic information of the user, wherein the basic information includes user personal information, historical behavior data, and historical social data. The user personal information includes the user's gender, age, and occupation. The historical behavior data includes historical browsing records, historical click records, historical search data, and historical purchase data. The historical social data includes the user's interactive behavior on the social platform, including the number of likes, comment content, and sharing data. S402, adding tags to the user's personal information, historical behavior data, and historical social data to obtain a user tag set, determining a feature vector set based on the user tag set, and constructing a user portrait based on the user tag set and the feature vector set; S403, constructing a user feature matrix based on the user's behavioral characteristics and consumption characteristics, and identifying the feature type of the user portrait through a clustering model, specifically hierarchical clustering; S404: extract the feature keywords of the feature type, identify the portrait feature factors in the feature type, and then obtain the user feature recognition result, and calculate the user feature coefficient R (G, F) according to the following formula: , where e1 and e2 are preset proportional coefficients, n represents the number of samples corresponding to the image feature factor, Gi is the i-th sample value of the parameter corresponding to other factors in the image feature factor, is the sample mean of the parameters corresponding to other factors in the image feature factor, Fi is the i-th sample value of the parameters corresponding to other factors in the image feature factor, It is the sample mean of the corresponding parameters of other factors in the portrait feature factor. The user feature coefficient is used to indicate the correlation between the user and the corresponding feature type. The larger the user feature coefficient is, the higher the correlation with the corresponding feature type is. Conversely, the smaller the user feature coefficient is, the lower the correlation with the corresponding feature type is.

[0020] The feedback analysis module is used to obtain the user's behavior data on the trial operation simulation interface, build a user behavior table based on the behavior data, analyze the user behavior table through a deep learning model to obtain user behavior analysis data, evaluate the trial operation simulation interface according to the user characteristic coefficient and the user behavior analysis data to obtain an evaluation result, and send it to the interface optimization unit; The specific process of evaluating the trial operation simulation interface is as follows: S501, obtaining user behavior data on the trial operation simulation interface, wherein the behavior data includes a user ID and a corresponding user characteristic coefficient, a routed page, a stay time, a link jump number, and a function usage number; S502: construct a timeline based on the user's operation time, and mark the behavior data according to the corresponding time nodes on the timeline to obtain a user behavior table, analyze the user behavior table, including aggregation comparison and sorting, to obtain user behavior analysis data, the user behavior analysis data including user ID, number of clicks on high-frequency pages, number of clicks on high-frequency links, and number of clicks on high-frequency functions; The specific aggregation process uses the dwell time as a judgment factor, records the dwell time through the attribute of the routed page, and then accumulates the dwell time of the same user on the same routed page. Then, the dwell time is sorted from large to small to obtain the top data as the high-frequency page. S503: Obtain user feature coefficients, and calculate the correlation coefficients Ui between the portrait feature factors in the user feature coefficients and the user behavior analysis data one by one according to the following formula: , where d is the preset weight coefficient, N is the total number of user operations, and fi is the number of clicks on high-frequency pages or high-frequency links or high-frequency functions; S504, obtaining a preset association judgment threshold, if the association coefficient Ui is greater than or equal to the association judgment threshold, then the evaluation result of the trial operation simulation interface meets the user's needs; If the correlation coefficient Ui is less than the correlation judgment threshold, the evaluation result of the trial operation simulation interface is that it does not meet the user needs.

[0021] The design optimization unit is used to obtain and process the evaluation results, and based on the evaluation results, perform user interface responsive design optimization on the page modules corresponding to the trial operation simulation interface to adapt to different user needs, and obtain the target user interface and send it to the design display unit again.

[0022] The specific operations for responsive design optimization of the trial run simulation interface are as follows: Adjusting the interface layout of the trial operation simulation interface; Adding new controls in the trial run simulation interface; Deleting existing controls from the trial run simulation interface; Adjusting the control properties of the controls in the trial operation simulation interface; Adjusting the control style of the control in the trial operation simulation interface; The link position of the control that needs to jump in the trial operation simulation interface is adjusted.

[0023] The present invention identifies each natural language command through a large language model, determines the design requirements expressed by each natural language command, and determines the corresponding design system, page image and page template based on the design requirements, generates a trial run user interface design draft and displays it to the user end, establishes a user portrait based on the user's basic information, and obtains the user characteristic coefficient. At the same time, a user behavior table is constructed based on the behavior data, and the user behavior table is analyzed through a deep learning model, and then the trial run simulation interface is evaluated to obtain an evaluation result, and based on the evaluation result, the page module corresponding to the trial run simulation interface is optimized for user interface responsiveness to meet different user needs.

[0024] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0025] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Visual communication design and display system based on model analysis, characterized by: It includes design requirement analysis unit, design output unit, design display unit, user feedback analysis unit and design optimization unit; The design requirement analysis unit includes a model recognition module and a requirement analysis module, wherein the model recognition module is used to obtain natural language commands, and recognize each natural language command through a large language model, determine the design requirements expressed by each natural language command, and send them to the requirement analysis module, wherein the large language model is obtained by training based on structured knowledge in the field of user interface design; The requirement analysis module is used to obtain design requirements, and determine the corresponding design system, page image and page template based on the design requirements, use the resources of the design system to construct the pseudo code corresponding to the page template, and send the pseudo code to the design output unit; The design output unit is used to convert the pseudo code into a corresponding vector design draft, and determine the corresponding style and filling content based on the page image, fill the vector design draft with the style and the filling content, and output the obtained trial user interface design draft to the design display unit; The design display unit is used to obtain the user interface design draft of the trial run and the target user interface, and generate a trial run simulation interface based on the user interface design draft of the trial run, and display the trial run simulation interface to the user end, and generate an actual operation interface based on the target user interface, and display the actual operation interface to the user end; The user feedback analysis unit includes a user analysis module and a feedback analysis module. The user analysis module is used to obtain basic information of the user, establish a user portrait according to the basic information, and classify the user portrait according to the clustering model to obtain a user feature recognition result. The user feature coefficient is calculated based on the user feature recognition result and sent to the feedback analysis unit; The feedback analysis module is used to obtain the user's behavior data on the trial operation simulation interface, build a user behavior table based on the behavior data, analyze the user behavior table through a deep learning model to obtain user behavior analysis data, evaluate the trial operation simulation interface according to the user characteristic coefficient and the user behavior analysis data to obtain an evaluation result, and send it to the interface optimization unit; The design optimization unit is used to obtain and process the evaluation results, and based on the evaluation results, perform user interface responsive design optimization on the page modules corresponding to the trial operation simulation interface to adapt to different user needs, and obtain the target user interface and send it to the design display unit again.

2. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific process of obtaining a large language model is as follows: S101, the natural language command includes user needs and goals, functional requirements, platform and device characteristics, visual design principles, interaction methods, accessibility, brand and visual identity, and technical feasibility; S102, constructing a knowledge graph based on data from different natural language command sources, using a depth-first search algorithm to obtain a language entity for each entity based on a search path corresponding to each entity in each knowledge graph, and performing cleaning and word segmentation on the language entity to obtain training data; S103, using a clustering algorithm to obtain the relevance of training data for each knowledge graph, and obtaining target task data corresponding to the training data; S104. Build a large language training model base based on the Transformer architecture, input the training data into the large language training model base for pre-training to obtain a pre-trained large language model, and fine-tune the pre-trained model using the target task data to obtain an optimized large language model.

3. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific process of getting the user interface design draft for trial operation is as follows: S201, retrieving associated styles and filling contents from a network database based on the page image, and integrating the styles and filling contents into a candidate data set; S202, calculating the correlation coefficient between each style and filling content in the candidate data set and the design requirement according to the clustering algorithm, and expressing it by a specific numerical value, and marking the correlation coefficient at the subscript of the style and filling content; S203, rearrange the styles and filling contents in the candidate data sets according to the correlation coefficients from large to small, and fill the several groups of styles and filling contents arranged in the front row into the vector design draft one by one to obtain the pseudo user interface design draft; S204, inputting the proposed user interface design draft into the target model, obtaining the verification result output by the target model, and selecting the user interface design draft for trial operation based on the verification result; The target model is a neural network model obtained by iterative training of a number of normal user interface design drafts and a number of abnormal user interface design drafts, wherein the abnormal user interface design drafts are user interface components that do not conform to preset design rules.

4. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific process of generating the trial run simulation interface is as follows: S301, obtaining interface configuration data corresponding to the user interface design draft, generating an initial user interface based on the interface configuration data, wherein the interface configuration data is used to represent the style, filling content and page template of the user interface design draft, and the initial user interface is displayed in the user terminal; S302: Configure the initial user interface to generate a trial operation simulation interface, where the trial operation simulation interface matches the user interface design draft.

5. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific process of calculating the user characteristic coefficient is as follows: S401. Obtaining basic information of the user, wherein the basic information includes user personal information, historical behavior data, and historical social data. The user personal information includes the user's gender, age, and occupation. The historical behavior data includes historical browsing records, historical click records, historical search data, and historical purchase data. The historical social data includes the user's interactive behavior on the social platform, including the number of likes, comment content, and sharing data. S402, adding tags to the user's personal information, historical behavior data, and historical social data to obtain a user tag set, determining a feature vector set based on the user tag set, and constructing a user portrait based on the user tag set and the feature vector set; S403, constructing a user feature matrix based on the user's behavioral characteristics and consumption characteristics, and identifying the feature type of the user portrait through a clustering model, specifically hierarchical clustering; S404: extract the feature keywords of the feature type, identify the portrait feature factors in the feature type, and then obtain the user feature recognition result, and calculate the user feature coefficient R (G, F) according to the following formula: , where e1 and e2 are preset proportional coefficients, n represents the number of samples corresponding to the image feature factor, Gi is the i-th sample value of the parameter corresponding to other factors in the image feature factor, is the sample mean of the parameters corresponding to other factors in the image feature factor, Fi is the i-th sample value of the parameters corresponding to other factors in the image feature factor, It is the sample mean of the corresponding parameters of other factors in the portrait feature factor. The user feature coefficient is used to indicate the correlation between the user and the corresponding feature type. The larger the user feature coefficient is, the higher the correlation between the user and the corresponding feature type is. On the contrary, the smaller the user feature coefficient is, the lower the correlation between the user and the corresponding feature type is.

6. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific process of evaluating the trial operation simulation interface is as follows: S501, obtaining user behavior data on the trial operation simulation interface, wherein the behavior data includes a user ID and a corresponding user characteristic coefficient, a routed page, a stay time, a link jump number, and a function usage number; S502: construct a timeline based on the user's operation time, and mark the behavior data according to the corresponding time nodes on the timeline to obtain a user behavior table, analyze the user behavior table, including aggregation comparison and sorting, to obtain user behavior analysis data, the user behavior analysis data including user ID, number of clicks on high-frequency pages, number of clicks on high-frequency links, and number of clicks on high-frequency functions; S503: Obtain user feature coefficients, and calculate the correlation coefficients Ui between the portrait feature factors in the user feature coefficients and the user behavior analysis data one by one according to the following formula: , where d is the preset weight coefficient, N is the total number of user operations, and fi is the number of clicks on high-frequency pages or high-frequency links or high-frequency functions; S504, obtaining a preset association judgment threshold, if the association coefficient Ui is greater than or equal to the association judgment threshold, then the evaluation result of the trial operation simulation interface meets the user's needs; If the correlation coefficient Ui is less than the correlation judgment threshold, the evaluation result of the trial operation simulation interface is that it does not meet the user needs.

7. The visual communication design and display system based on model analysis according to claim 1, characterized in that: The specific operations for responsive design optimization of the trial run simulation interface are as follows: Adjusting the interface layout of the trial operation simulation interface; Adding new controls in the trial run simulation interface; Deleting existing controls from the trial run simulation interface; Adjusting the control properties of the controls in the trial operation simulation interface; Adjusting the control style of the control in the trial operation simulation interface; The link position of the control that needs to jump in the trial operation simulation interface is adjusted.

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