Intangible cultural heritage digital display method

Abstract

The invention discloses an intangible cultural heritage digital display method, and relates to the technical field of data processing, and the method comprises the following steps: building a quantitative evaluation system, classifying intangible cultural heritage to obtain classification results which are specifically divided into a class A, a class B and a class C. The class A is a dynamic class, the class B is a contextual class, and the class C is a static class; differentiated data acquisition schemes are deployed according to categories, data acquisition is carried out on the intangible cultural heritage, knowledge graph construction is carried out on the acquired data, and digitization of the intangible cultural heritage is completed; by performing quantitative evaluation and classification on the intangible cultural heritage, the classification accuracy can be increased, the repeated purchase cost of equipment can be reduced, a classified multi-modal acquisition scheme can be designed, heterogeneous data right confirmation and sharing can be realized by adopting a block chain cross-chain protocol, and the cultural context of the intangible cultural heritage can be completely restored. Cross-mechanism data islands are broken, the microcosmic feature digitization precision is improved, and a multi-branch narrative system is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method for digital display of intangible cultural heritage. Background Art

[0002] Currently, the digital display of intangible cultural heritage mainly relies on technologies such as 3D modeling, virtual reality (VR / AR), and multimedia databases. Among them, 3D modeling technology constructs static models through laser scanning or photogrammetry for digital archiving of cultural relics, but it is limited to the morphological reproduction of utensil - type intangible cultural heritage and is difficult to capture the continuous movements of dynamic skills (such as dance and handicrafts); VR / AR technology creates an immersive experience through virtual scenes, but it relies on special devices such as helmets and handles, and most of the content is preset scripts, lacking space for users' independent exploration; Multimedia databases record the processes of intangible cultural heritage in the form of audio - video, but the information is stored fragmentarily, and no semantic associations are established among cultural elements, resulting in "skills being divorced from context" (for example, only showing embroidery stitches without reflecting the ethnic beliefs behind the patterns); However, in actual use, relying on technologies such as 3D modeling can only reproduce the form of utensils, unable to capture the spatio - temporal continuity of dynamic skills (such as dance movements and handicraft processes). The resolution of micro - feature collection is limited to the 0.1mm level, and single - modality data (such as independent audio - video) leads to the lack of association of cultural elements and insufficient context restoration; Moreover, the penetration rate of VR devices is insufficient, and the hardware cost hinders popularization. At the same time, the preset scripts limit independent exploration, resulting in a short user retention time and difficulty in attracting users. Summary of the Invention

[0003] To solve the above problems, the present invention provides a method for digital display of intangible cultural heritage.

[0004] A method for digital display of intangible cultural heritage includes the following steps: Step 1: Establish a quantitative evaluation system, classify intangible cultural heritage, and obtain classification results, specifically divided into Class A, Class B, and Class C, where Class A is dynamic, Class B is context - type, and Class C is static; Step 2: According to the classification results, deploy differentiated data collection schemes by category, collect data on intangible cultural heritage, and construct a knowledge graph for the collected data to complete the digitization of intangible cultural heritage; Step 3: According to the classification results, formulate a display scheme for each intangible cultural heritage; Step 4: Detect the users who interact with the display plan of each intangible cultural heritage, quantitatively analyze the user behavior, obtain the detection results of each user, and optimize based on the detection results of each user.

[0005] Preferably, the specific working steps of the above Step 1 are as follows: Calculate the dynamic index D, context dependence C, and interaction feasibility I of each intangible cultural heritage; If D is greater than or equal to 0.7 and I is greater than or equal to 0.6, then classify this intangible cultural heritage as Class A; If C is greater than 0.5 and does not meet the condition that D is greater than or equal to 0.7 and I is greater than or equal to 0.6, then classify this intangible cultural heritage as Class B; If it does not meet either Class A or Class B, then classify this intangible cultural heritage as Class C.

[0006] Preferably, the specific steps for calculating the dynamic index D of each intangible cultural heritage are as follows: Install an inertial sensor on the elbow of the inheritor of the intangible cultural heritage to obtain the standard deviation W of the joint angle change during the performance of this intangible cultural heritage; Obtain the time length T of the performance of this intangible cultural heritage, and identify the number N of key frames of the video through the OpenPose algorithm; For each intangible cultural heritage, according to the formula , calculate and obtain the dynamic index D of each intangible cultural heritage.

[0007] Preferably, the specific steps for calculating the context dependence C of each intangible cultural heritage include the following: Obtain the number N1 of entities related to the intangible cultural heritage skills extracted from the oral history text; According to the formula , calculate and obtain the context dependence C of each intangible cultural heritage, where M is the number of environmental parameters, is the correlation coefficient between the environmental parameters and the skill quality of each intangible cultural heritage.

[0008] Preferably, the specific steps for calculating the interaction feasibility I of each intangible cultural heritage include the following: Obtain the standardized value R of the equipment cost of each intangible cultural heritage; Obtain the number N2 of interactive elements of each intangible cultural heritage; According to the formula , calculate and obtain the interaction feasibility I of each intangible cultural heritage.

[0009] Preferably, the specific working steps of Step 2 are as follows: For Class A intangible cultural heritage, use high-precision motion capture equipment to record the dynamic actions in the intangible cultural heritage skills, capture the movement trajectories and action details of human joints, deploy multi-parameter sensors to specifically control temperature sensors, humidity sensors, and light sensors, synchronously collect environmental data, and record the environmental conditions during the skill performance or production process; For Class B intangible cultural heritage, use 3D scanning technology to perform 3D modeling on the sites and related items of the intangible cultural heritage skills, record the spatial layout and physical characteristics; use natural language processing technology to perform semantic analysis on the oral history text, and extract cultural elements and semantic associations related to the skills; For Class C intangible cultural heritage, use ultra-macroscopic scanning equipment, high-resolution microscopes, and electron microscopes to perform high-precision scanning on the microscopic features in the intangible cultural heritage skills; Collect data according to a predetermined plan, fuse multi-modal data, and build a unified data storage and management platform; Extract key features from multi-modal data, such as text vectors, visual vectors of pictures, spectral features of audio, etc.; Use the extracted features to build a knowledge graph to represent the entities, relationships, and attributes in the intangible cultural heritage projects.

[0010] Preferably, the specific working steps of Step 3 are as follows: For Class A intangible cultural heritage, through the motion capture data, display the dynamic process of the intangible cultural heritage skills in real time, and through the environmental sensing data, display the environmental changes during the skill performance or production process; For Class B intangible cultural heritage, through the 3D modeling data, display the sites and related items of the intangible cultural heritage skills, and through the semantic analysis results, display the cultural background and semantic associations of the intangible cultural heritage skills; For Class C intangible cultural heritage, through the ultra-macroscopic scanning data, display the microscopic features of the intangible cultural heritage skills, and through the spectral analysis data, display the physical and chemical properties of the materials.

[0011] Preferably, the specific working method of Step 4 is as follows: Collect user behavior data through the user's interaction behaviors on the platform, including clicks, dwell time, and searches, and collect user feedback data through user evaluations, comments, etc.; Extract the features of user behavior, specifically including the number of clicks J1, dwell time J2, and number of searches J3, to obtain user behavior data; Through the user behavior data, calculate the preference degree of the user for different contents, and obtain the preference value P1 of the user behavior for the intangible cultural heritage. Obtain the target value P2 of the preference value of the user behavior for the intangible cultural heritage set in advance; According to the difference between the preference value P1 of the user behavior for the intangible cultural heritage and the target value P2 of the preference value of the user behavior for the intangible cultural heritage, regularly adjust the actual value to make it closer to the target value; Specifically, use the difference between the preference value P1 of the user behavior for the intangible cultural heritage and the target value P2 of the preference value of the user behavior for the intangible cultural heritage multiplied by 0.5 to obtain the weight for increasing the recommended content.

[0012] Preferably, the specific working method of obtaining the preference value P1 of the user behavior for the intangible cultural heritage is as follows: According to the formula , calculate and obtain the preference value P1 of the user for each intangible cultural heritage.

[0013] Beneficial effects: By quantitatively evaluating and classifying intangible cultural heritages, the classification accuracy can be increased, and the repeated procurement cost of equipment can be reduced. A multi-modal acquisition scheme for different categories can be designed, and the blockchain cross-chain protocol can be used to realize the confirmation of rights and sharing of heterogeneous data. The cultural context of intangible cultural heritages can be completely restored, the cross-institutional data islands can be broken, the digital accuracy of micro-features can be improved, a multi-branch narrative system can be constructed, and dynamic content recommendation based on user behavior can be realized; Contextual association jump driven by knowledge graph, implanting a cultural fidelity constraint mechanism, specifically including an automatic verification module for user-created content and semantic locking of key cultural symbols, can reduce equipment dependence, expand the audience coverage, provide a non-linear cultural exploration path, and ensure the authenticity of cultural dissemination. Brief Description of the Drawings

[0014] Figure 1 is the flowchart of the method of the present invention. Detailed Embodiments

[0015] However, in the actual use process, relying on technologies such as 3D modeling can only reproduce the form of artifacts, and cannot capture the spatio-temporal continuity of dynamic skills (such as dance movements, handicraft processes). The resolution of micro-feature acquisition is limited to the 0.1 mm level, and single-modal data (such as independent audio and video) results in the lack of association of cultural elements and insufficient context restoration; Such as Figure 1 shown: A method for digital display of intangible cultural heritage, characterized by including the following steps: Step 1: Establish a quantitative evaluation system, classify intangible cultural heritages, and obtain classification results, specifically divided into Class A, Class B, and Class C, where Class A is dynamic, Class B is contextual, and Class C is static; It should be noted that traditional classification relies on subjective experience, fails to consider the dynamic characteristics of intangible cultural heritage, resulting in technological mismatches, and has a high degree of blindness in equipment investment. Through the quantitative evaluation and classification of intangible cultural heritage, this technical solution can increase the accuracy of classification and reduce the procurement cost of duplicate equipment; Step 2: According to the classification results, deploy differential data acquisition schemes by category, collect data on intangible cultural heritage, and construct a knowledge graph for the collected data to complete the digitization of intangible cultural heritage; It should be noted that existing technologies use single-modal data acquisition (such as only using cameras to record actions), resulting in the lack of semantic associations between cultural elements (such as not recording data on the natural ecological environment related to skills), heterogeneous data formats across institutions, inability to interconnect, and insufficient micro-feature acquisition capabilities (such as traditional photogrammetry being unable to capture the micro-texture of embroidery silk threads); This technical solution can design multi-modal acquisition schemes by category, use blockchain cross-chain protocols to achieve the confirmation of rights and sharing of heterogeneous data, can completely restore the cultural context of intangible cultural heritage, break the data islands across institutions, and improve the digital accuracy of micro-features; Step 3: According to the classification results, formulate a display plan for each intangible cultural heritage; It should be noted that existing technologies rely on professional hardware devices (such as VR helmets), have a low popularization rate among the public, have a single interaction mode (such as only supporting linear browsing), poor user autonomy, fragmented cultural information presentation, and lack of overall cognitive support; This technical solution can construct a multi-branch narrative system, with dynamic content recommendation based on user behavior, context association jumps driven by knowledge graphs, and implant a cultural authenticity constraint mechanism, specifically including an automatic verification module for user-generated content and semantic locking of key cultural symbols, which can reduce equipment dependence, expand the audience coverage, provide a non-linear cultural exploration path, and ensure the authenticity of cultural dissemination; Step 4: Detect users who interact with the display plan of each intangible cultural heritage, quantitatively analyze user behavior, obtain the detection results of each user, and optimize based on the detection results of each user.

[0016] It should be noted that in existing technologies, user behavior data is not effectively utilized, system updates rely on manual intervention, the response speed is slow, the content is highly homogeneous, and there is a lack of a user co-creation ecosystem; This technical solution realizes system self-optimization and rapid iteration by constructing a multi-dimensional user portrait model and designing a closed-loop optimization system, improving content diversity and attractiveness.

[0017] As an optional embodiment: The specific working steps of Step 1 are as follows: Calculate the dynamic index D, context dependence C, and interaction feasibility I for each intangible cultural heritage; If D is greater than or equal to 0.7 and I is greater than or equal to 0.6, then classify this intangible cultural heritage as Class A; If C is greater than 0.5 and does not meet the conditions that D is greater than or equal to 0.7 and I is greater than or equal to 0.6, then classify this intangible cultural heritage as Class B; It should be noted that 0.7, 0.6, and 0.5 are pre-set basic thresholds and can be adjusted according to needs during actual use; If it does not meet either Class A or Class B, then classify this intangible cultural heritage as Class C. It should be noted that the logical loophole in the classification rules is solved to ensure that all intangible cultural heritage items can be uniquely classified, and the technical resource allocation is strictly matched with the project characteristics.

[0018] As an optional embodiment: The specific steps for calculating the dynamic index D for each intangible cultural heritage are as follows: Install an inertial sensor on the elbow of the inheritor of the intangible cultural heritage to obtain the standard deviation W of the joint angle change during the performance of this intangible cultural heritage; it should be noted that in this embodiment, the standard deviation W of the joint angle change is the standard deviation of the elbow joint angle; Obtain the time length T of the performance of this intangible cultural heritage and identify the number of key frames N of the video through the OpenPose algorithm; For each intangible cultural heritage, according to the formula , calculate and obtain the dynamic index D for each intangible cultural heritage.

[0019] As an optional embodiment: The specific steps for calculating the context dependence C for each intangible cultural heritage include the following: Obtain the number N1 of entities related to the intangible cultural heritage skills extracted from the oral history text; it should be noted that in this embodiment, it can be extracted through the intangible cultural heritage BERT model; According to the formula , calculate and obtain the environmental dependence C for each intangible cultural heritage, where M is the number of environmental parameters, is the correlation coefficient between the environmental parameter and the quality of each intangible cultural heritage skill. It should be noted that is calculated through the correlation analysis of multi-parameter sensor data and expert scores. Specifically, is a value between -1 and 1, indicating the strength of the linear relationship between the environmental parameter and the skill quality, taking the absolute value It is to ensure that the impact of relevance is positive, because both positive and negative correlations have an impact on the quality of the art. M is the type of environmental parameters. In this embodiment, only humidity and light are considered as environmental parameters, so the value of M is 2.

[0020] As an alternative embodiment: The specific steps for calculating the interaction feasibility I of each intangible cultural heritage include the following: Obtain the standardized value R of the equipment cost for each intangible cultural heritage; it should be noted that price information of relevant equipment is collected from the market quotation database, the minimum and maximum values of the equipment cost are determined for normalization processing, and the equipment cost is normalized to the range of 0-1 to obtain the standardized value R of the equipment cost; Obtain the number N2 of interactive elements for each intangible cultural heritage; it should be noted that in this embodiment, the specific value-taking method is to clarify the definition and scope of interactive elements, such as gestures, tools, voice commands, etc. By inviting multiple staff members to evaluate the intangible cultural heritage projects respectively, list all possible interaction points, and take the average value of the proposed quantity as the final number of interactive elements; According to the formula , calculate and obtain the interaction feasibility I of each intangible cultural heritage.

[0021] As an alternative embodiment: The specific working steps of step two are as follows: For type A intangible cultural heritage, use high-precision motion capture equipment to record the dynamic actions in the intangible cultural heritage art, capture the movement trajectories and action details of human joints, deploy multi-parameter sensors to specifically control temperature sensors, humidity sensors and light sensors, synchronously collect environmental data, and record the environmental conditions during the art performance or production process; For type B intangible cultural heritage, use 3D scanning technology to perform 3D modeling on the venues and related items of the intangible cultural heritage art, record the spatial layout and physical characteristics; use natural language processing technology to perform semantic analysis on the oral history text and extract cultural elements and semantic associations related to the art; For type C intangible cultural heritage, use ultra-macroscopic scanning equipment, high-resolution microscopes, and electron microscopes to perform high-precision scanning on the microscopic features in the intangible cultural heritage art; Collect data according to a predetermined plan, fuse the multi-modal data, and build a unified data storage and management platform; it should be noted that use multi-modal AI data intelligent solutions such as MatrixOne Intelligence to realize the fusion storage and management of multi-modal data; Extract key features from multimodal data, such as text vectors, visual vectors of pictures, spectral features of audio, etc. It should be noted that tools such as MatrixGenesis are used for feature extraction and embedding generation; Construct a knowledge graph using the extracted features to represent the entities, relationships, and attributes in intangible cultural heritage projects. It should be noted that a graph database (such as Neo4j) or a knowledge graph construction tool (such as TensorFlow Knowledge Graph) can be used for construction; It should also be noted that through multimodal data collection and knowledge graph construction, the cultural context of intangible cultural heritage is fully restored, including information in multiple aspects such as techniques, environment, symbols, etc. The blockchain cross-chain protocol is used to achieve the rights confirmation and sharing of heterogeneous data, break the cross-institutional data silos, promote data interconnection and interoperability, and through technologies such as ultra-macroscopic scanning and material spectral analysis, improve the digital accuracy of microscopic features and better record and protect the details of intangible cultural heritage techniques.

[0022] As an optional embodiment: The specific working steps of step three are as follows: For Class A intangible cultural heritage, the dynamic process of intangible cultural heritage techniques is displayed in real time through motion capture data, and the environmental changes during the technique performance or production process are displayed through environmental sensing data; For Class B intangible cultural heritage, the venues and related items of intangible cultural heritage techniques are displayed through 3D modeling data, and the cultural background and semantic associations of intangible cultural heritage techniques are displayed through semantic analysis results; For Class C intangible cultural heritage, the microscopic features of intangible cultural heritage techniques are displayed through ultra-macroscopic scanning data, and the physical and chemical properties of materials are displayed through spectral analysis data.

[0023] As an optional embodiment: The specific working method of step four is as follows: Collect user behavior data through the user's interaction behaviors on the platform, including clicks, dwell time, and searches, and collect user feedback data through user evaluations, comments, etc.; Extract the features of user behavior, specifically including the number of clicks J1, dwell time J2, and number of searches J3, to obtain user behavior data. It should be noted that the ranges of the number of clicks, dwell time, and number of searches are times, seconds, and times respectively; Calculate the preference degree of the user for different contents through user behavior data to obtain the preference value P1 of user behavior for intangible cultural heritage; Obtain the target value P2 of the preference value of user behavior for intangible cultural heritage set in advance; Regularly adjust the actual value according to the difference between the preference value P1 of intangible cultural heritage based on user behavior and the target value P2 of the preference value of intangible cultural heritage based on user behavior, so that it is closer to the target value. Specifically, multiply the difference between the preference value P1 of intangible cultural heritage based on user behavior and the target value P2 of the preference value of intangible cultural heritage based on user behavior by 0.5 to obtain the weight for increasing recommended content. It should be noted that to improve the user's preference for content, for example, the weight of the recommended content can be increased by 0.1.

[0024] As an optional embodiment: The specific working method of obtaining the preference value P1 of intangible cultural heritage based on user behavior is as follows: According to the formula , calculate and obtain the preference value P1 of the user for each intangible cultural heritage.

[0025] Working principle: Step 1: Establish a quantitative evaluation system, classify intangible cultural heritage, and obtain classification results, which are specifically divided into Category A, Category B, and Category C, where Category A is dynamic, Category B is context-based, and Category C is static; It should be noted that traditional classification relies on subjective experience, does not consider the dynamic characteristics of intangible cultural heritage, resulting in technology mismatches, and there is a large blindness in equipment investment. This technical solution can increase the accuracy of classification and reduce the repeated procurement cost of equipment by quantitatively evaluating and classifying intangible cultural heritage; Step 2: According to the classification results, deploy a differentiated data collection plan by category, collect data on intangible cultural heritage, and construct a knowledge graph for the collected data to complete the digitization of intangible cultural heritage; It should be noted that the existing technology uses single-modal data collection (such as only using a camera to record actions), resulting in the lack of semantic associations between cultural elements (such as not recording the natural ecological environment data related to skills), heterogeneous data formats across institutions, inability to interconnect, and insufficient micro-feature collection capabilities (such as traditional photogrammetry being unable to capture the micro-texture of embroidery silk); This technical solution can design a multi-modal collection plan by category, use the blockchain cross-chain protocol to achieve the confirmation of rights and sharing of heterogeneous data, can completely restore the cultural context of intangible cultural heritage, break the data island across institutions, and improve the digital accuracy of micro-features; Step 3: According to the classification results, formulate a display plan for each intangible cultural heritage; It should be noted that the existing technology relies on professional hardware devices (such as VR helmets), with low popularization rate among the public, single interaction mode (such as only supporting linear browsing), poor user autonomy, fragmented cultural information presentation, and lack of overall cognitive support; This technical solution can build a multi-branched narrative system, with dynamic content recommendation based on user behavior, contextual association jumps driven by a knowledge graph, and implant a cultural fidelity constraint mechanism, specifically including an automatic verification module for user-created content and semantic locking of key cultural symbols, which can reduce device dependence, expand the audience coverage, provide a non-linear cultural exploration path, and ensure the authenticity of cultural dissemination; Step 4: Detect users who interact with the display solution of each intangible cultural heritage, quantitatively analyze user behavior, obtain the detection results of each user, and optimize based on the detection results of each user.

[0026] The above is only the preferred implementation mode of the present invention. The protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of this template.

Claims

1. A method for digital display of intangible cultural heritage, characterized in that: The following steps are involved: Step 1: Establish a quantitative evaluation system to classify intangible cultural heritage and obtain classification results, which are specifically divided into Class A, Class B and Class C, among which Class A is dynamic, Class B is contextual, and Class C is static; Step 2: Based on the classification results, deploy differentiated data collection plans by category, collect data on intangible cultural heritage, and construct a knowledge graph for the collected data to complete the digitization of intangible cultural heritage; Step 3: Based on the classification results, formulate a display plan for each intangible cultural heritage; Step 4: Detect users who interact with each intangible cultural heritage display scheme, quantify and analyze user behavior, obtain the detection results of each user, and optimize based on the detection results of each user.

2. The method for digital display of intangible cultural heritage according to claim 1, characterized in that: The specific working steps of step one are as follows: The dynamic index D, context dependence C and interactive feasibility I of each intangible cultural heritage are calculated; If D is greater than or equal to 0.7 and I is greater than or equal to 0.6, the intangible cultural heritage is classified as Category A; If C is greater than 0.5, and does not satisfy D greater than or equal to 0.7, and I greater than or equal to 0.6, the intangible cultural heritage is classified as Category B; If it does not meet the requirements of either Category A or Category B, the intangible cultural heritage shall be classified as Category C.

3. The method for digital display of intangible cultural heritage according to claim 2, characterized in that: The specific steps of calculating the dynamic index D of each intangible cultural heritage are as follows: Install inertial sensors on the elbows of the inheritors of the intangible cultural heritage to obtain the standard deviation W of the joint angle changes during the performance of the intangible cultural heritage; The duration T of the intangible cultural heritage performance is obtained, and the number of key frames N of the video is identified through the OpenPose algorithm; For each intangible cultural heritage, according to the formula , calculate and obtain the dynamic index D of each intangible cultural heritage.

4. The method for digital display of intangible cultural heritage according to claim 2, characterized in that: The specific steps for calculating the contextual dependence C of each intangible cultural heritage include the following: Obtain the number of entities N1 related to intangible cultural heritage skills extracted from oral history texts; According to the formula , calculate and obtain the environmental dependence C of each intangible cultural heritage, where M is the number of environmental parameters, is the correlation coefficient between environmental parameters and the quality of each intangible cultural heritage skill.

5. The method for digital display of intangible cultural heritage according to claim 2, characterized in that: The specific steps to calculate the interactive feasibility I of each intangible cultural heritage include the following: Obtain the standardized value R of the equipment cost of each intangible cultural heritage; Obtain the number of interactive elements N2 of each intangible cultural heritage; According to the formula , calculate and obtain the interactive feasibility I of each intangible cultural heritage.

6. The method for digital display of intangible cultural heritage according to claim 1, characterized in that: The specific working steps of step 2 are as follows: For Category A intangible cultural heritage, high-precision motion capture equipment is used to record the dynamic movements in the intangible cultural heritage skills, capture the movement trajectory and movement details of human joints, deploy multi-parameter sensors to specifically control temperature sensors, humidity sensors and light sensors, synchronously collect environmental data, and record the environmental conditions during the performance or production process; For Category B intangible cultural heritage, 3D scanning technology is used to create 3D models of the sites and related objects of intangible cultural heritage skills, and to record the spatial layout and physical characteristics; Use natural language processing technology to conduct semantic analysis on oral history texts and extract cultural elements and semantic associations related to skills; For Category C intangible cultural heritage, ultra-macro scanning equipment, high-resolution microscopes, and electron microscopes are used to perform high-precision scanning of the microscopic features of the intangible cultural heritage techniques; Collect data according to the predetermined plan, integrate multimodal data, and build a unified data storage and management platform; Extract key features from multimodal data, such as text vectors, visual vectors of images, spectral features of audio, etc. The extracted features are used to construct a knowledge graph to represent the entities, relationships, and attributes in the intangible cultural heritage items.

7. The method for digital display of intangible cultural heritage according to claim 1, characterized in that: The specific working steps of step three are as follows: For Category A intangible cultural heritage, motion capture data is used to display the dynamic process of intangible cultural heritage skills in real time, and environmental sensor data is used to display environmental changes during the performance or production of skills; For Category B intangible cultural heritage, the sites and related objects of intangible cultural heritage skills are displayed through 3D modeling data, and the cultural background and semantic association of intangible cultural heritage skills are displayed through semantic analysis results; For Category C intangible cultural heritage, ultra-macro scanning data is used to display the microscopic characteristics of intangible cultural heritage skills, and spectral analysis data is used to display the physical and chemical properties of materials.

8. The method for digital display of intangible cultural heritage according to claim 1, characterized in that: The specific working method of step 4 is as follows: Collect user behavior data through user interactions on the platform, including clicks, dwell time, and searches, and collect user feedback data through user evaluations and comments; Extract the characteristics of user behavior, including the number of clicks J1, the dwell time J2 and the number of searches J3, to obtain user behavior data; Through user behavior data, calculate the user's preference for different contents and obtain the user's preference value P1 for intangible cultural heritage; Obtaining a preset target value P2 of the preference value of the user behavior for the intangible cultural heritage; According to the difference between the preference value P1 of the user behavior for the intangible cultural heritage and the target value P2 of the preference value of the user behavior for the intangible cultural heritage, the actual value is adjusted regularly to make it closer to the target value; Specifically, the weight for adding recommended content is obtained by multiplying the difference between the preference value P1 of the user behavior for intangible cultural heritage and the target value P2 of the preference value of the user behavior for intangible cultural heritage by 0.

5.

9. The method for digital display of intangible cultural heritage according to claim 8, characterized in that: The specific working method of obtaining the preference value P1 of the user behavior for the intangible cultural heritage is as follows: According to the formula , calculate and obtain the user's preference value P1 for each intangible cultural heritage.

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