96 results about "Association mining" patented technology

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

The invention relates to the technical field of information security data processing, and discloses an information security analysis method and system based on big data, and the method comprises the steps: S1, collecting multi-source heterogeneous security related data which comprises a business log, a user behavior track, network traffic, an application program interface calling record, an identity authentication log and a real-time security data flow, preprocessing the collected data to obtain standardized data; and S2, performing entity identification, event extraction and relationship mining based on the standardized data, and constructing a cross-modal threat knowledge graph containing security entity nodes and associated edges. The method solves the problem of monitoring blind areas caused by lack of dynamic association mining capability among data in a traditional method, and particularly aims at distributed, low-frequency and multi-stage hidden attacks, the scheme can accurately recover an attack chain and identify high-risk threats through dynamic matching and path reasoning of a knowledge graph, and the method has a good application prospect. And the detection coverage rate and accuracy in a complex attack scene are remarkably improved.

The invention discloses a fault diagnosis method and system based on a multi-source data association rule and a graph neural network. The method comprises the steps of extracting high-frequency operation data and low-frequency time sequence state data based on historical data, and establishing an equipment operation feature set; an Apriori algorithm is utilized to screen correlation characteristics to calculate a correlation relation, and a fault symptom set is constructed; and taking the association relationship of the features as an adjacent matrix embedded graph neural network, and training the constructed fuzzy graph neural network based on historical data to obtain a fault diagnosis model. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a feature screening module, a feature association relationship analysis module, a fault diagnosis model training module, a fault diagnosis module and a database storage module, and can perform multi-source data fusion analysis and training and updating of a fault diagnosis model. According to the method, the fault diagnosis model is constructed by combining multi-source data fusion, feature extraction, association relationship mining and the fuzzy graph neural network, so that more accurate and efficient fault diagnosis is realized.

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

The invention provides an intelligent environment damage questionnaire generation and semantic analysis method and system, and the method comprises the steps: obtaining structured environment data through a government department interface, and capturing network public data in combination with an AI search engine; performing cleaning and normalization processing on the data, and constructing a standardized environment database; constructing a semantic model based on an NLP technology, dynamically generating a customized investigation problem according to an interview object type and an event scene classifier, and optimizing the priority of the problem; extracting a key entity from the interview text by adopting a pre-training model; mapping the entity to a preset pollution category through the environmental damage ontology library, and generating a dynamic causal association map; and verifying the integrity and consistency of the atlas by using a logic verification rule, and automatically outputting a structured survey report. According to the method, the defects that a traditional questionnaire is rigid in design, fragmented in information and insufficient in implicit association mining are overcome, the accuracy and efficiency of environmental damage investigation are remarkably improved, and intelligent support is provided for pollution traceability and responsibility definition.

The invention relates to the field of resource association analysis, and discloses a knowledge graph-based archive decision support and association analysis method, which comprises the following steps of: firstly, acquiring structured and unstructured archive data; preprocessing is carried out through data cleaning, duplicate removal and format conversion, semantic disambiguation is carried out on historical fuzzy words in unstructured data, and entity normalization processing is carried out on multi-category names of the same character; secondly, based on the preprocessed structured data, entities, relationships and attributes are extracted, and entity association evidences are supplemented through a cross-modal alignment technology; performing consistency verification, and storing the verified entity relationship network into a graph database to form an archive knowledge graph; then, in combination with a graph traversal and time sequence analysis algorithm, a hidden association path is mined, and trend prediction is carried out; and finally, generating a decision evidence chain according to an association mining result. And establishing a knowledge graph dynamic updating mechanism, and automatically reconstructing the entity association network when a file or a historical research result is newly added.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

The invention discloses a multi-modal data processing method and device based on a large model, equipment and a medium. Performing feature extraction and association mining on the multi-modal data to obtain multi-modal data features; according to a retrieval sequence of the multiple text matching methods, sequentially using the multiple text matching methods to perform text retrieval on the multi-modal data features in a pre-constructed knowledge base to obtain a first associated sub-graph; based on the semantic similarity between the multi-modal data features and multi-modal data in a pre-constructed knowledge base and a similarity threshold value, determining a second associated sub-graph; performing standard evaluation on the plurality of candidate results in the first associated sub-graph and the second associated sub-graph, and sorting the plurality of candidate results according to an evaluation result to obtain a retrieval result; inputting the retrieval result and the multi-modal data features into a large model for information analysis to obtain prompt information and a target tool; and sending the prompt information and the target tool to the terminal, and receiving a data processing result of the terminal.

The invention provides an online value evaluation system based on real estate, and belongs to the technical field of online evaluation, and the system comprises an online data obtaining module which is used for obtaining a basic feature vector and multi-modal evaluation data of a target real estate online; the feature extraction and mining module is used for carrying out space mapping on the explicit features and the defect features and carrying out potential association mining on the implicit features, and obtaining high-association implicit description in combination with the basic feature vector; the evaluation processing module is used for identifying real evaluation, false evaluation and fuzzy evaluation, and correcting the fuzzy evaluation after preliminary analysis according to a spatial mapping result and high-correlation implicit description in combination with social platform subject discussion data; and the value evaluation module is used for obtaining a comprehensive value according to the basic value reference line, the real evaluation and the correction evaluation, and outputting a value evaluation report. The problems that real estate evaluation is difficult to distinguish virtuality and reality and is fuzzy and difficult to solve are effectively solved, and the evaluation result can be comprehensive and accurate.

The invention discloses a lost customer behavior characteristic mode association mining method and device, a medium and a product, and relates to the technical field of financial intelligent prediction, and the method comprises the steps: carrying out the preprocessing of a preset lost customer data set, and obtaining a transaction data table, the lost customer data set comprises at least one group of lost customer behavior characteristics and at least one lost mode; determining a frequent item set according to the transaction data table, extracting association rules in the frequent item set, and determining a key association rule from the association rules; determining a mapping relationship between the lost customer behavior feature combination and the lost mode according to the key association rule; and querying a target contact loss mode corresponding to the to-be-predicted contact loss customer behavior feature combination in the mapping relationship, and generating a risk early warning strategy according to the target contact loss mode. According to the method, the complex association between the lost customer behavior characteristics and the lost mode can be analyzed.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

The invention relates to the technical field of data management, in particular to a cross-platform advertisement putting and SEO collaborative optimization system which comprises a resource time sequence scheduling module, a keyword screening module, a putting path construction module, a data conflict comparison module and a content archiving and auditing module. According to the invention, through association mining of multi-platform advertisement data and user behaviors, real-time mapping of time sequence distribution of put resources and user group states, and dynamic integration of budget weights and audience portraits in a keyword selection process, a multi-parameter interactive screening mechanism is formed for advertisement paths; text similarity judgment and conflict source tracing are synchronously introduced into channel content, an archiving standard is jointly defined by content change frequency and source credibility, data in each link automatically adjusts an attribution boundary according to a change trend, multi-source data validity is accurately distinguished in an archiving process, and structure-level linkage is realized by advertisement circulation, content integration, path adaptation and conflict detection. And the consistency and uniqueness of data updating and archiving are guaranteed.

The invention relates to the technical field of big data mining, in particular to a big data text retrieval mining system and method.The method comprises the steps that an original text data set input by a user is obtained; performing association mining on the structured feature matrix based on a graph neural network to obtain a knowledge association graph; receiving a to-be-retrieved text; inputting the text to be retrieved into a pre-trained multi-mode encoder to obtain a composite query vector; performing association expansion on the candidate texts based on the knowledge association graph to obtain an expanded text set; and carrying out importance sorting on the extended text set to obtain a final retrieval result set. According to the method, through cross-modal feature mapping and multi-modal data fusion, different types of data can be comprehensively analyzed; according to the characteristics, the retrieval range can be widened, the accuracy of a retrieval result can be improved, and particularly, when other modal data except a text are processed, potential correlation among the data can be fully mined.

The embodiment of the invention provides a three-dimensional point cloud data analysis method and system based on artificial intelligence, and relates to the technical field of three-dimensional point cloud data analys.The method comprises the steps that firstly, a three-dimensional point cloud data set containing space coordinates, a collection time sequence and other space-time attributes is obtained, and then a three-dimensional point cloud fragment space-time association evolution structure is constructed; the spatial-temporal association degree is determined and dynamically updated through a spatial-temporal neighborhood cooperative propagation mechanism, then multi-level cross-fragment feature interaction enhancement processing is executed, multi-dimensional spatial-temporal features are extracted and fused to generate an enhanced cross-fragment feature interaction vector, then a pre-training model is called, multiple rounds of global association mining are performed through a spatial-temporal multi-layer interaction module, and the spatial-temporal association degree is obtained. And finally, the space-time structure association rule is analyzed to generate an application instruction, and the application instruction is sent to the target processing terminal, so that the three-dimensional point cloud data analysis efficiency is improved.

The invention discloses a multi-modal data association relationship mining method based on potential feature perception, which belongs to the field of multi-modal data analysis and feature association modeling in artificial intelligence and data mining technologies, and comprises the steps of multi-modal data acquisition and preprocessing, multi-modal feature mining based on potential semantic alignment, multi-modal data analysis and feature association modeling. Performing multi-stage feature fusion and time sequence association representation learning, and constructing a cross-modal semantic association graph. According to the method, under the conditions of noise interference, unbalanced sample distribution and weak semantic association of the multi-modal data, robust fusion and semantic consistency expression of the multi-modal features in a potential space can be realized through adaptive anomaly correction and a multi-level feature alignment mechanism, mismatching caused by noise pollution and shallow association is avoided, and the robustness of the multi-modal features is improved. And accurate mining of the high-order potential semantic relationship is realized. Meanwhile, the semantic edge and the time sequence edge can be subjected to separation modeling according to the internal structure of the multi-modal data under the conditions of modal isomerism and time sequence overlapping, and meanwhile, a unified cross-modal association graph is constructed. Furthermore, in order to improve the accuracy of time sequence relation modeling, time sequence comparative learning and dynamic consistency constraint are utilized, effective distinguishing between real time sequence dependence and multi-mode repeated representation is achieved, and the precision and robustness of multi-mode correlation analysis are remarkably improved.

The invention relates to the technical field of artificial intelligence, and particularly provides an answer generation method, system and device based on an RAG technology and a storage medium, and the method comprises the steps: receiving a question text input by a user; based on the question text, performing semantic retrieval in a pre-constructed knowledge base, calculating semantic similarity between the question text and knowledge fragments in the knowledge base, and retrieving target knowledge fragments of which the semantic similarity meets a preset threshold value; performing splicing processing on the question text and the target knowledge fragment to form an input prompt word; and inputting the input prompt word into a pre-trained generative large language model, generating a corresponding answer text by the generative large language model, and outputting the answer text. According to the method, three breakthroughs of semantic understanding, association mining and result optimization are realized, and multi-modal retrieval is supported.

The invention relates to a space-time alignment and semantic association modeling method for cross-platform geographic information data, and belongs to the field of big data and natural language processing. The method comprises the following steps: step 1, carrying out space-time uncertainty modeling and probabilistic representation, and quantifying time and space uncertainty of multi-source data; 2, combining an alignment decision mechanism with adaptive learning to complete multi-evidence intelligent flexible alignment fusion; step 3, carrying out deep semantic analysis and real-time association discovery based on multi-modal semantic understanding and association mining; and 4, dynamically updating and reasoning the knowledge graph, and performing real-time analysis and real-time reasoning. According to the method, for a sudden search task, a new data source can be rapidly integrated, the knowledge graph is enriched, global situation visual control is achieved, multilevel reasoning based on the knowledge graph is achieved, efficient and accurate geographic information data intelligence is supported, and the task response efficiency is improved.

The invention discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, and relates to the technical field of reinforcement learning, and the method comprises the steps: inputting an initial data packet into a dynamic interaction model, carrying out explicit association mining through a semantic enhancement layer, optimizing a rule matching path through a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topological structure derivation on the dynamic commodity knowledge graph to generate a graph topological analysis report and a metadata list, and performing knowledge reasoning integration on the graph topological analysis report and the metadata list to generate an intelligent navigation engine; calling an intelligent navigation engine to execute multi-path semantic query and rule verification on the dynamic knowledge graph to generate a candidate classification scheme set; and performing multi-target collaborative optimization on the candidate classification scheme set to generate a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. According to the invention, through the dynamic interaction model and multi-target collaborative optimization, the rule adaptation efficiency in a complex scene is improved.

The embodiment of the invention provides a multi-modal data processing method and system based on a knowledge graph, and aims to solve the problems of semantic association deficiency and potential association mining difficulty in multi-source information processing. The method comprises the following steps: firstly, acquiring a multi-source information set containing different expression form information units and source identifiers, and performing entity mapping on the multi-source information set and a preset semantic association network to generate an information unit set with semantic association tags; performing cross-source feature association processing to obtain a comprehensive feature set; then, a semantic reasoning rule is called for association extension, and an extension feature set is generated; and finally, based on the extended feature set, generating an information processing result conforming to an application scene and feeding back the information processing result to a corresponding interface, thereby effectively integrating multi-source information and mining potential association.

The invention relates to the technical field of intelligent fire protection and big data analysis, and discloses a knowledge graph construction and association analysis method and system for fire protection supervision and communication global data. In order to solve the technical problems of heterogeneous fire-fighting data and single association, the invention provides a domain ontology-driven knowledge graph automatic construction and deep analysis method. The core innovation of the method is that a multi-source heterogeneous data fusion model is adopted to unify semantic representation of multi-modal data such as fire-fighting documents and Internet of Things perception; performing entity relationship joint extraction by utilizing the pre-training model, and efficiently constructing a fire-fighting knowledge graph of massive triples; and a graph neural network is introduced to carry out deep association mining and reasoning, a risk conduction path is quantitatively evaluated, and potential threats are predicted. According to the invention, discrete fire-fighting data is improved into a computable and reasonable knowledge system, and deep perception and intelligent decision-making of a fire-fighting safety situation are realized.

The invention discloses a search optimization system based on knowledge association of a knowledge base, which improves the correlation, comprehensiveness and depth of search results, and meets the high-quality knowledge retrieval requirements under the scenes of intelligent search, knowledge management and the like. According to the technical scheme, the system comprises a user interaction layer module used for achieving direct interaction between a user and the system, receiving query input and displaying optimized search results; the core function layer module is used for completing intention accurate analysis, deep association mining, quantitative extension retrieval and association dominant sorting by taking knowledge association as driving force; the knowledge association support layer module is used for providing underlying technical support of knowledge association mining, quantification and sorting; and the auxiliary data layer module is used for providing auxiliary data for field adaptation and user personalization so as to enhance the processing precision of the core function layer module.

The invention discloses an agricultural supply chain risk prediction method and system based on a knowledge graph, and the method comprises the steps: synchronously collecting business data, environment meteorological data and market public opinion data at each business node of an agricultural supply chain, and writing the data into an edge buffer region according to a unified time identifier; on the basis of the improved agricultural supply chain reference model, entity recognition, relation extraction and attribute standardization are performed on the collected data, and a heterogeneous instance knowledge graph is constructed; markov chain random walk path sampling is carried out on each node, low-dimensional representation learning is carried out on a sampling result by adopting a cold ice self-adaptive embedding algorithm, and a risk representation vector of each node is generated; and inputting a risk prediction model jointly constructed based on a grey theory and a Markov state transition theory, calculating a risk score of each supply chain node, and outputting a grading result. According to the method, the problems of dynamic analysis, deep association mining and real-time accurate prediction of the risk of each node of the agricultural supply chain are effectively solved.

The invention discloses a distribution and micro collaborative low-voltage diagnosis method and system based on digital twinborn and association mining. The method comprises the following steps: constructing a digital twinborn model of a distribution and micro collaborative power distribution network; analyzing low-voltage spatial-temporal distribution characteristics, screening a governance area, simulating the governance area by using a digital twinborn model, selecting a corresponding governance strategy to adjust the digital twinborn model for simulation again after mining a low-voltage cause, and verifying a governance effect of the selected governance strategy based on a simulation result; and for the governance strategy with the governance effect meeting the requirement, calculating a comprehensive score according to the corresponding simulation result, and taking the governance strategy with the highest comprehensive score as a low-voltage governance strategy. According to the method, the distribution micro-grid collaborative digital twin is constructed, diagnosis and treatment of the low-voltage problem are carried out based on the digital twin and association mining, and the voltage treatment level of the distribution micro-grid is improved.

The invention provides a knowledge graph construction method and system for intelligently fusing multi-modal data, and the system employs a layered architecture, and comprises a data input layer, a mode special extractor, a multi-modal aligner, a graph construction / mining device, and a multi-modal retrieval service layer. The modular design enables the system to have good expandability and maintainability, and each module can be independently developed and optimized. The characteristics of different modal data are fully utilized, and the efficiency and accuracy of feature extraction are improved. Through the accurate alignment processing, the accuracy of entities and relationships in the knowledge graph can be effectively improved, and the alignment error is reduced. The deep association relationship among the entities in the basic knowledge graph is mined through the configured deep association mining engine, so that potential knowledge hidden in data can be found, and the content of the knowledge graph is enriched. A user can obtain knowledge from multiple angles, different application requirements are met, and the utilization efficiency of the knowledge is improved.

The application discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, relates to the technical field of reinforcement learning, and comprises the following steps: inputting initial data packets into a dynamic interaction model, performing explicit association mining in a semantic enhancement layer, optimizing rule matching paths in a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topology structure derivation on the dynamic commodity knowledge graph, generating a graph topology analysis report and a metadata list, integrating knowledge reasoning on the graph topology analysis report and the metadata list, and generating an intelligent navigation engine; calling the intelligent navigation engine to perform multi-path semantic query and rule verification on the dynamic knowledge graph, generating a candidate classification scheme set; performing multi-objective collaborative optimization on the candidate classification scheme set, generating a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. The application improves the rule adaptation efficiency in complex scenarios through the dynamic interaction model and multi-objective collaborative optimization.

The invention provides a network security analysis method and device, equipment and a medium, and relates to the technical field of computers. In the application, firstly, based on an operation log of target network equipment, a corresponding equipment relation graph is constructed; secondly, based on the device access information corresponding to other access devices in the device relation graph, carrying out association mining on the device access information corresponding to the target access device, and outputting target access information features; and then, based on the target access information features, carrying out network security analysis on the target access equipment, and outputting a target security analysis result. Based on the content, the problem of relatively low reliability of network security analysis in the prior art can be improved.

The application provides a kind of intelligent environmental damage questionnaire generation and semantic analysis method and system, comprising: obtaining structured environmental data through government department interface, combining AI search engine to capture network public data;Data is cleaned and normalized, and a standardized environmental database is constructed;Based on NLP technology, a semantic model is constructed, customized survey questions are dynamically generated according to the type of interviewee and event scene classifier, and question priority is optimized;Key entities are extracted from interview text using a pre-trained model;Through the environmental damage ontology library, the entity is mapped to the preset pollution category, and a dynamic causal correlation graph is generated;The integrity and consistency of the graph are verified using logical verification rules, and a structured survey report is automatically output.The application solves the defects of traditional questionnaire design rigidity, information fragmentation and insufficient implicit association mining, significantly improves the accuracy and efficiency of environmental damage investigation, and provides intelligent support for pollution tracing and responsibility definition.

The invention discloses a microbial data semantic processing method and system based on a graph model, and relates to the technical field of knowledge graph and microbial information processing, and the method comprises the steps: obtaining microbial data from a heterogeneous data source, and carrying out the standardization processing; constructing a domain ontology model conforming to microbial taxonomy specifications; implementing entity disambiguation and unified identifier mapping by utilizing a pre-training language model; a semantic relation triple is extracted in a mode of combining remote supervision and deep learning; constructing a microorganism knowledge attribute graph and storing the graph in a graph database; knowledge reasoning and function prediction are carried out by using a graph attention network; the method supports natural language semantic retrieval, and solves the problems that heterogeneous microorganism data integration is difficult, the naming disambiguation precision is insufficient and the function association mining capacity is limited.

The invention provides a bid inviting and purchasing penetration type supervision AI large model construction method and system fused with multi-modal data. The method belongs to the technical field of artificial intelligence and intelligent auditing. The method comprises the steps of performing multi-source data fusion processing on internal system data and external data related to bid invitation purchasing, generating a cross-domain associated data set, performing implicit associated feature mining, obtaining implicit associated data among bidders, deploying a graph neural network, and constructing an association relationship mining network. According to the industrial design review method based on artificial intelligence, through multi-source data fusion and implicit association mining, design details and potential association can be accurately captured, and the comprehensiveness and accuracy of design review are improved.

The invention relates to the technical field of material informatics, and discloses a method for constructing a knowledge graph based on a material database, which comprises the following steps: S1, collecting multi-source basic data and carrying out standardization processing, identifying a knowledge graph entity based on the standardized data and establishing entity attributes, defining an explicit association relationship between the entities and generating corresponding relationship data; s2, performing association mining on the basis of the explicit association relationship and the entity attributes to obtain a hidden association relationship used for representing potential relationships between entities; and S3, fusing the entity, the explicit association relationship and the hidden association relationship, and constructing a material database knowledge graph. According to the method, raw materials, a formula, a process, performance and an application scene serve as an entity framework, explicit modeling is conducted on the relation between entities, and a computable association network is constructed, so that research and development personnel can achieve cross-link retrieval, reasoning and positioning based on unified semantics, and the efficiency and consistency of formula development and performance analysis are improved.