378 results about "Bipartite graph" patented technology

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention provides a self-adaptive intent perception enhanced graph contrast learning recommendation method, which comprises the following steps of: constructing a user-article bipartite graph, carrying out multi-layer embedded coding on a user and an article by adopting a graph neural network, and obtaining a multi-intention embedded representation of Gaussian distribution through a variational auto-encoder in combination with a multi-intention hypothesis; noise disturbance is adaptively added to intention embedding so as to enhance feature robustness, and the method respectively implements comparative learning of isomorphic and heterogeneous nodes in a node interaction space domain and an intention perception domain, so that the problems of data sparsity and intention entanglement are effectively relieved; and finally, carrying out joint optimization on recommendation task loss, KL divergence loss and double-domain comparison loss, and realizing accurate modeling and recommendation of the personalized preference of the user. Experimental results show that the method is superior to a mainstream recommendation system on a plurality of real data sets, and has strong generalization ability and robustness.

The invention relates to the technical field of recommendation algorithms, provides a graph collaborative filtering course recommendation method based on interactive attention and comparative learning, and aims to solve the problem that a traditional recommendation system is insufficient in modeling ability in a sparse interaction scene. The method comprises the following steps: firstly, constructing a user-course bipartite graph, and utilizing a dynamic attention mechanism guided by an interactive opposite-end node: carrying out vector dot product through original embedding of the opposite-end node (for example, course embedding is used during user aggregation) and current embedding of a neighbor node, and generating an attention coefficient in combination with temperature parameter normalization; and multi-level structure information aggregation is realized. Afterwards, local context features are fused through a multilayer graph convolutional network, random noise disturbance is introduced to generate a multi-view comparison sample, the consistency of positive samples is maximized in combination with an InfoNCE loss function, and the robustness of the model to noise and sparse data is enhanced; and finally, optimizing user-course embedding in combination with Bayesian personalized sorting loss and comparison loss, and generating a personalized recommendation list. According to the method, the key interaction relationship is screened through guided attention, the representation discrimination is improved in combination with comparative learning, and the recommendation precision in cold start and data sparse scenes can be improved.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

The invention discloses a large language model semantic enhancement collaborative filtering recommendation method based on comparative learning, and belongs to the field of recommendation systems.The method comprises the steps that a user-item bigraph is constructed, user-item semantic information collaborative information is provided through GNN, user-item semantic information is extracted through cue words, a deterministic topology view enhancement strategy is adopted, and user-item semantic information collaborative filtering recommendation is achieved. According to the method, a semantic neighbor extension view is generated, a semantic neighbor reconstruction view is generated, collaboration and semantic information alignment are performed, double-view structure alignment is performed, and a loss function is integrally trained, so that the accuracy and the cold start capability of a recommendation system are remarkably improved through collaborative graph structure learning and semantic enhancement.

The invention discloses a smart building energy consumption data monitoring management method and system, and relates to the technical field of smart building and energy management, and the method comprises the steps: constructing a dynamic graph structure, extracting joint features through employing a graph attention mechanism, weighting the Mahalanobis distance of a node through employing a batch average attention coefficient, and obtaining an abnormal scene feature vector; a target classification function is defined, an EPC-PSO algorithm is used for optimization, and a Softmax classifier is used for classifying abnormal scene feature vectors; defining an energy consumption efficiency objective function, decomposing into a sub-problem of each device by using Lagrange, outputting a global initial strategy vector by using a gradient descent method, defining a smart building task, constructing a matrix of a comprehensive benefit weight, converting the device and the task into a bipartite graph problem, and solving by using a Hungary KM algorithm; the batch average attention coefficient weights the mahalanobis distance, the robustness of anomaly detection is enhanced, and the comprehensive benefit of resources is improved by using Lagrange decomposition, a gradient descent method and a Hungary KM algorithm.

The invention discloses a multi-modal collaborative recommendation method, system and device, a medium and a program product, and relates to the technical field of multi-modal recommendation, and the method comprises the steps: carrying out the feature enhancement of a user portrait and a commodity attribute through a large-scale language model, and obtaining a more comprehensive and precise text modal representation through generating more abundant semantic description; a modal reference vector is introduced, and the similarity between the modal features and the modal reference vector is calculated to evaluate the importance of different modals, so that modal noise is filtered, a user-commodity bipartite graph and a commodity-commodity similarity graph based on the modal features are constructed, and user preferences are captured from user-commodity interaction and commodity-commodity semantic relationships; by integrating a difference perception attention mechanism and a dynamic modal preference gating mechanism, fine-grained fusion of multi-modal features is realized, so that the model can adaptively adjust the importance of each modal according to different scenes.

A method of evaluating an ability of a large language model (LLM) to generate an accurate hypothesis model (HM) of a linear programming word problem (LPWP) is provided. An attributed bipartite graph of the HM is transformed into an attributed bipartite graph of the ground truth model of the LPWP. The number of transformation steps required for the transformation effectively measures the ability of the LLM to generate an accurate HM. The use of attributed bipartite graphs provide invariance in the order to LPWP variables and constraints. Corresponding apparatus and systems are also provided.

A method for selecting training data for training a deep learning model is provided. The method includes steps of: (a) obtaining one or more individual attributes each of which corresponds to each of a plurality of training data included in total training data, and generating a bipartite graph by matching each of the plurality of training data included in the total training data with the individual attributes; and (b) selecting n training data among the total training data, by referring to the bipartite graph, wherein the n is a target number of the training data to be used for training the deep learning model, and wherein the training data selecting device selects the n training data to be used for training the deep learning model such that each cardinal number of each of the individual attributes matched with the n training data is within a predetermined deviation threshold.

The invention relates to an identification method for matching scene behaviors by using multi-modal features, and belongs to the technical field of scene behavior matching. The method comprises the following steps: acquiring multi-modal scene behavior data, and carrying out noise self-adaptive purification processing on the multi-modal scene behavior data to obtain a preprocessed scene image, scene audio data and a scene label text; secondly, performing feature collaborative extraction on the preprocessed data to obtain visual features, audio features and text features, inputting the cooperatively extracted features into a scene behavior matching network, and performing scene feature fusion and behavior feature fusion respectively to obtain a scene feature vector and a behavior feature vector; and constructing a bipartite graph according to the scene feature vector and the behavior feature vector, calculating the semantic similarity between nodes of the bipartite graph, dynamically updating the edge weight of the bipartite graph according to the semantic similarity between the nodes, and normalizing the updated bipartite graph to obtain a scene behavior matching result. According to the method, the association degree of the scene and the behavior can be accurately quantified, and the accuracy of a matching result is greatly improved.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention belongs to the technical field of data processing, and particularly provides a bipartite graph subgraph mining-based product quality problem correlation analysis method, which comprises the following steps of: obtaining a product and quality-related data; formatting and cleaning the data; extracting data related to quality to perform label extraction; establishing a bipartite graph model according to the association relationship between the product and the quality label; and performing correlation analysis on the product quality problem based on a bipartite graph sub-graph mining method. According to the method, data resources are integrated, and a bipartite graph sub-graph mining algorithm is optimized, so that the system is suitable for large-scale data analysis, experts can conveniently carry out correlation analysis and optimize a manufacturing process, and tracing of product quality problems and mining of products with potential quality problems are facilitated. In addition, the method is also helpful for predicting and identifying products with possible quality problems, and the product quality is improved.

The invention discloses a pneumovirus gene data multi-view clustering integration method and device and electronic equipment. The method comprises the following steps: constructing a width self-encoder with a width learning network and a global subspace structure; training a width auto-encoder, determining model parameters of the width auto-encoder, inputting multi-view pneumonia gene data into the trained width auto-encoder, and outputting a compressed feature subspace; clustering the compressed feature subspaces to obtain a plurality of basic partitions as integration members of an integration pool; the basic partition is converted into a fuzzy partition matrix, an undirected bipartite graph is constructed based on the integration pool, and connection weights between nodes of the undirected bipartite graph correspond to values in the fuzzy partition matrix; and cutting the undirected bipartite graph into several disjoint sub-graphs, and obtaining a clustering integration result of multi-view pneumonia gene data according to the sample nodes of each sub-graph. According to the method, parameter quantity and computing resources are remarkably reduced, and multi-view clustering analysis of pneumonia gene data is efficiently completed.

The invention relates to the technical field of cargo transportation optimization, and discloses a combined fleet cargo transportation optimization method and system based on data processing, and the method comprises the following steps: S101, collecting cargo feature data and ship feature data, and respectively representing the data through first feature data and second feature data; s102, preprocessing the first feature data and the second feature data to obtain third feature data, fourth feature data and fifth feature data; s103, constructing a matching degree prediction model according to the improved graph neural network model, and outputting the matching degree of the cargo to the ship; the cargo-ship bipartite graph structure comprises cargo nodes, ship nodes, edges, initial features of the cargo nodes, initial features of the ship nodes and initial features of the edges; and S104, optimizing the allocation scheme according to the improved genetic algorithm to obtain an optimal allocation scheme. The efficiency, accuracy and flexibility of cargo and ship distribution are improved.

The invention discloses a non-coding RNA (Ribonucleic Acid) and disease association prediction method based on a graph neural network, and belongs to a biomolecule association prediction technology in the field of biological information. The invention breaks through the limitation of a single task of a conventional method, and provides a novel multi-task learning framework based on task migration. Potential LDA, MDA and LMI can be deduced by effectively utilizing the known complex relationship among lncRNA, miRNA and diseases. A plurality of graph structures are constructed, including a bipartite graph, a feature structure graph and a meta-path graph. The synergistic effect of the multi-graph structure significantly enriches the context information of the nodes, and effectively improves the prediction accuracy of the model. Pairwise attribute learning is introduced and is used for capturing the direct relationship among lncRNA, miRNA and diseases. According to the method, the prediction performance is improved, overfitting is effectively reduced, and the robust generalization ability of the model is ensured.

The invention discloses a complex network importance node evaluation method based on group interaction. The method comprises the following specific steps: step 1, carrying out community division on a network through a Louvain algorithm; step 2, constructing a Louvain bipartite graph; step 3, obtaining an outgoing transfer matrix and an incoming transfer matrix; step 4, obtaining an extended transfer matrix according to the outgoing transfer matrix and the incoming transfer matrix; 5, expanding the transfer matrix to obtain an enhanced transfer matrix; step 6, calculating the importance score of each node; 7, arranging the nodes in the network in a descending order according to the importance score of each node; and step 8, identifying important nodes of the network through a network disintegration algorithm. According to the method, based on group interaction, the interaction between nodes which are not directly connected is considered, the random walk is expanded to the Louvain bipartite graph, and the concepts of outgoing walk and incoming walk are added; and in combination with a network disintegration algorithm, the key nodes in the network are efficiently and accurately identified, and the robustness and the stability are high.

The invention relates to the technical field of knowledge recommendation, and discloses a knowledge recommendation method and system based on cross-modal comparison and Riemannian hyperbolic geometry, and the method comprises the steps: obtaining a user and project interaction bipartite graph; obtaining a basic knowledge graph; combining the user and project interaction bipartite graph with the basic knowledge graph to obtain a collaborative knowledge graph; multi-modal features are set for each item in the collaborative knowledge graph, and the multi-modal features comprise visual modal features and text modal features; inputting the collaborative knowledge graph and the multi-modal features of the items into the trained knowledge recommendation model, and recommending the items to the user through the trained knowledge recommendation model; according to the trained knowledge recommendation model, potential information among different modals is further mined by using cross-modal comparative learning, and an extra supervision signal is introduced for a recommendation task. Movie items or game items that the user is interested in can be recommended to the user.

The invention relates to an APT attack detection and machine learning technology, in particular to an APT attack detection method fusing comparative learning and cross-domain recommendation. A traceability graph is constructed based on a data set, and a bipartite graph is constructed according to the traceability graph; generating an r-ego network for all nodes in the bipartite graph; generating positive and negative sample pre-training graph encoders in the source domain, and transferring the pre-trained graph encoders to the target domain; generating initialization embedding in a target domain by using a pre-trained graph encoder; the matrix decomposition model is finely adjusted by using the initialized embedding, and the final embedding of each node in the target domain is obtained by using the finely-adjusted matrix decomposition model; and predicting whether the two nodes interact based on the final embedding. According to the method, the high-order connectivity is formed by using the side information of the system entities to predict the possibility of interaction between the entities. And meanwhile, a cross-domain recommendation method is used, so that the problem of data sparsity of APT attacks is relieved while the recommendation performance on a target domain is improved.

The invention discloses a graph encoder recommendation method based on guide diffusion model enhancement, and the method comprises the specific steps: S1, initializing a user-article interaction adjacency matrix, carrying out the normalization processing, and generating a Laplacian matrix; s2, adaptively calculating a mask probability for each click behavior, generating a partially visible mask bipartite graph, taking the mask bipartite graph as a mask graph encoder input in a model training process, and enhancing a supervision signal from a structure level; and S3, generating representations of the user and the article by using a mask graph encoder, performing de-noising processing by adding directed noise and introducing a diffusion model guided by a cooperative signal, and enhancing a supervision signal from a semantic level. And S4, generating user and article representations by using a graph encoder, optimizing semantic information of the representations, and finally predicting articles which may be clicked by each user. According to the method, by combining a structure and semantics dual enhancement strategy, the accuracy and recall rate of a recommendation system are remarkably improved, and meanwhile, the robustness of the system to noise information is improved.

The invention discloses a bipartite graph link prediction system based on edge weight guided comparative learning and a recommendation method thereof, and relates to the technical field of recommendation systems. The system comprises a data preprocessing unit, an embedding unit, a weight-guided side attention unit, a double-layer contrast learning unit, a fusion learning unit and a prediction unit. Based on the system, neighbor information is dynamically aggregated through a weight-guided side attention mechanism, so that the model can adaptively pay attention to an important connection relationship; a double-layer contrast learning framework is adopted, and local interaction modes and global structure similarity are comprehensively captured; a weight-degree balance negative sampling strategy is provided, high-quality negative samples are generated, and the negative samples are excellent in performance in the sparse network; a unified learning framework is provided, classification and comparison learning targets are fused, and optimization of local and global structures is realized. The method effectively improves the accuracy and efficiency of a recommendation system, is suitable for large-scale and sparse user-article interaction data, and has a wide application prospect.

Methods and server systems for generating task-agnostic representations for nodes in bipartite graph are described herein. Method performed by server system includes accessing bipartite graph including first set of nodes and second set of nodes. Herein, set of edges exist between first and second set of nodes. Method includes performing for each node of first and second set of nodes: identifying a natural neighbor node, the natural neighbor node being a two-hop neighbor node from the each node, Then, generating temporary representation for one-hop neighbor node based on set of features corresponding to the one-hop neighbor node Then, generating temporary neighbor node based on temporary representation for the one-hop neighbor node. Then, generating augmented neighborhood based on the natural node and the temporary neighbor node, and then determining via machine learning model, task-agnostic representation for the each node based on augmented neighborhood.

The invention discloses an anti-cancer drug sensitivity prediction method and system based on bipartite graph and hypergraph learning, and belongs to the technical field of computer software. The method comprises the following steps: acquiring cell line and drug data and drug and cell line reaction original IC50 values by using an open source database, and standardizing the IC50 values; constructing a bipartite graph of the cell line and the drug, and obtaining embedded representation of the drug and the cell line by applying a graph convolutional network; super nodes are introduced to construct a cell line-drug isomerism hypergraph, and node features are learned through GCN; and finally, fusing the output of the GCN layer by applying an attention mechanism, and predicting the final association probability of the cell line-drug pair in combination with a full-connection layer. According to the method, combined exploration and collaborative optimization of the bipartite graph and the hypergraph are achieved, meanwhile, potential and complex relations between the medicine and the cell line are explored through the hypergraph, the quality of node feature representation is jointly improved, and therefore the association score between the medicine and the cell line is predicted more accurately.

Methods and systems for learning representations for nodes of temporal bipartite graph. Method performed by server system includes accessing temporal bipartite graph including first nodes, and second nodes. Each first node has first features and each second node has second features. Method includes generating, by Graph Neural Network (GNN) model, first interim representations for each first node based on first features corresponding to each of a set of temporal two-hop neighbor nodes of each first node. Method includes computing first homogeneous representation for each first node based on first interim representations and first features of each first node. Method includes computing first global homogeneous representation for each first node based on first homogeneous representation for each first node. Method includes computing first local heterogeneous representation for each first node based on first homogeneous representation for each first node.

The invention discloses a recommendation system noise trimming and long tail enhancement method based on two-stage graph optimization. In order to solve the problems of noise interaction and long-tail user data sparsity in an implicit feedback recommendation system, the method comprises the following steps: firstly, constructing a user-article interaction bipartite graph, and initializing a graph convolutional network model to generate a preliminary embedded representation; in the first stage, the reliability of an interaction edge is evaluated through a node similarity index (Nsim), a noise edge is trimmed in combination with a dynamic threshold strategy, and a de-noised subgraph is generated to improve the embedding quality. And in the second stage, for the long-tail user, a probability sampling mechanism is adopted to add a high-confidence potential interaction edge, and an enhanced sub-graph is generated to improve the long-tail recommendation effect. Finally, Bayesian personalized ranking (BPR) loss is optimized through iterative training, and an accurate personalized recommendation result is generated. The accuracy and fairness of the recommendation system are remarkably improved, and the method is suitable for application scenes such as e-commerce, social media and content recommendation.

A multi-behavior e-commerce platform recommendation method based on collaborative comparative learning comprises the following steps: firstly, constructing a multi-behavior e-commerce user-e-commerce commodity interaction bipartite graph for an online e-commerce platform; then, a multi-behavior cascade residual network is used for capturing deep representation of the bipartite graph, and a multi-behavior attention network is used for capturing interactive representation of the bipartite graph; then, through cross-layer comparative learning, cross-behavior comparative learning and cross-network comparative learning, the representation performance of the e-commerce user-e-commerce commodity interaction bipartite graph is improved; meanwhile, Bayesian personalized sorting loss is calculated to improve recommendation performance; and finally, obtaining a final e-commerce user and e-commerce commodity representation so as to realize personalized commodity recommendation. According to the method, the behavior information and the multi-behavior interaction information are deeply mined, the representation performance of the e-commerce users and the e-commerce commodities is improved by using comparative learning, and a more accurate recommendation result which better conforms to the interest of the users can be generated for the e-commerce platform.

The invention relates to a joint simulation model execution sequence determination method based on an improved Tarjan algorithm. The joint simulation model execution sequence determination method comprises the following steps: constructing a weighted bigraph representing a dependency relationship between models; applying a multi-level weighted Tarjan algorithm to the weighted bipartite graph to identify strong connected components; processing the identified strongly connected components to eliminate loop dependencies; performing weighted topological sorting on the processed graph without the strong connected component; carrying out key path analysis; and generating an optimized model execution sequence based on a weighted topological sorting result and a critical path analysis result. According to the method, the requirement of strong-coupling multi-model joint simulation can be better met, and the execution sequence of the models is effectively determined.

The invention discloses a multi-view self-representation and clustering joint optimization deep clustering method, which comprises the following steps of: firstly, acquiring data samples of a plurality of views, and selecting a most representative sample from each view as an anchor point by adopting a VDA algorithm; constructing and pre-training an auto-encoder network; on this basis, a self-representation module is introduced, shared self-representation and view unique self-representation are learned at the same time, and self-representation of each view is constructed through the similarity between an anchor point and a sample; constructing comprehensive self-representation based on sharing and unique self-representation, constructing a bipartite graph affinity matrix, and obtaining an initial clustering result of samples and anchor points by adopting a bipartite graph clustering algorithm; a clustering result is fed back to the self-representation module, and the representation is iteratively corrected; and finally, sharing and view unique self-representation are fused, comprehensive self-representation is obtained and used for spectral clustering, and a final clustering result is obtained. According to the method, the consistency and diversity characteristics between the views are effectively combined, and the problems that a traditional method is insufficient in structure modeling and low in optimization efficiency are solved.

The invention discloses an underwater hyperspectral clustering method based on a bipartite graph, and relates to the technical field of underwater image processing. The method comprises the following steps: acquiring spectral images of a to-be-processed image in different spectral intervals, taking the spectral image of each spectral interval as a view, and constructing hyperspectral data based on the acquired views; constructing a hyperspectral clustering model based on subspace clustering learning, and processing the hyperspectral image based on the hyperspectral clustering model; the hyperspectral clustering model comprises an adaptive dynamic anchor point selection module, an anchor point centroid learning module and a bipartite graph decomposition module; and iterating the hyperspectral clustering model based on an alternative update variable strategy until an iteration stop condition is met, and outputting a clustering result based on the clustering indication index matrix. According to the method, a model based on subspace clustering learning is constructed, uniform self-adaptive anchor points are dynamically learned in all pixel points, and the optimal cluster with accurate pixel division can be directly obtained from bipartite graph decomposition.