317results about How to "Enhance explanatory" patented technology

The invention discloses a water quality prediction method based on graph neural network and spatial-temporal feature fusion, and the method comprises the steps: obtaining hydrological data and water quality data of a plurality of monitoring stations in a preset drainage basin in a preset time period, representing the monitoring stations as nodes of a graph, and representing the adjacency relation between the monitoring stations as edges of the graph, a graph topology network mapping multiple monitoring station pollutant data is constructed, a spatial-temporal feature fusion module is adopted to perform feature extraction and fusion on the graph topology network to obtain water pollution spatial-temporal feature data, and the spatial-temporal feature fusion module is obtained by training a graph convolutional network, a gating circulation unit and a self-attention mechanism. And performing fusion processing on the extracted feature data to form a time sequence data set of a graph topology structure, and finally predicting the data set through a graph time sequence prediction model to obtain water quality prediction results of the plurality of monitoring stations in a target time period. The method can effectively deal with the spatial and temporal change process of water pollution, reduce the water quality prediction cost and improve the water quality prediction precision.

The invention relates to a multi-modal data generalization learning method and system based on causal invariant transformation. The method comprises the following steps: acquiring multi-modal data to construct a causal graph; designing causal invariant transformation to simulate a causal relationship, keeping causal features in the causal relationship unchanged, changing non-causal features, and generating a new training sample; fusing information of different modes; and constructing a self-supervised learning framework, predicting labels of the node pairs as pre-training tasks, and learning feature representation and causal structures of the multi-modal data. A causal graph is constructed and analyzed, causal invariant transformation is designed to simulate a causal relationship and generate a new training sample, and the model can learn feature representation consistent with a causal structure, so that stable performance is kept on different distributed data; according to the method, a self-supervised learning framework is constructed, extra labeling information is not needed, data representation and causal structures are learned by predicting labels of node pairs as pre-training tasks, and the generalization ability of the model can be effectively improved.

The invention discloses an AI-driven abnormal transaction intelligent monitoring and analysis system and method, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining structured features and unstructured features, carrying out the feature fusion, and constructing an account feature vector; constructing a cross-domain heterogeneous graph, learning an embedded vector of an account node in the cross-domain heterogeneous graph based on a time sequence heterogeneous graph neural network, and generating an account portrait vector; training an abnormal transaction detection model, and predicting the abnormal probability of the current real-time transaction record of the account by taking the account portrait vector and the transaction record as input data; dividing all accounts into account groups based on the account portrait vectors, calculating a group abnormal transaction threshold value, and calculating an adaptive abnormal transaction threshold value based on the group abnormal transaction threshold value and the similarity between the account portrait vectors and the center vectors of the account groups to which the account portrait vectors belong; and if the abnormal probability is greater than a self-adaptive abnormal transaction threshold, marking the transaction of the account as an abnormal transaction, triggering risk control measures, and regularly updating the self-adaptive abnormal transaction threshold.

The invention discloses a photoelectric multi-mode decision fusion method and system for mental disorder recognition, relates to the technical field of mental disorder recognition, and performs decision fusion based on an improved machine learning algorithm by integrating photoelectric multi-mode data and collecting and analyzing various data in real time. Through effective fusion of result data of different modal recognition models, the accuracy and stability of mental disorder recognition are improved, meanwhile, the misjudgment rate of a single modal method is reduced, and the overall diagnosis efficiency is improved. The invention not only provides an innovative method, but also designs a corresponding fusion system, and provides a comprehensive solution for early recognition and diagnosis of mental disorders.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of artificial intelligence, and discloses a vehicle-mounted terminal-oriented vehicle behavior abnormal mode recognition method, which comprises the following steps of: acquiring sensing data of a vehicle-mounted terminal; performing feature extraction on the obtained data by adopting a full-connection neural network based on tree ring bionic optimization to obtain a first feature; performing feature dimension reduction on the first feature by using a self-encoding neural network based on mask enhancement to obtain a second feature; constructing a classifier model by adopting a fractional order neural network based on a quantum chaos theory, and training the classifier model by utilizing the second feature; and using the trained classifier model to carry out vehicle behavior abnormal mode identification. According to the invention, the recognition precision of the abnormal driving mode in practical application is improved.

The invention discloses a photovoltaic power prediction method based on seasonal weather classification and an RLHF large model, and the method comprises the following steps: 1, data acquisition: obtaining power generation data and historical meteorological data of a photovoltaic power station, and constructing a data set; 2, constructing a prediction model: constructing a weather classification and photovoltaic output prediction model, dividing a year into a plurality of seasons, dividing weather into a plurality of weather types, and constructing a photovoltaic output prediction model for each weather type of each season; an RLHF large model is constructed, a deep learning and reinforcement learning combined method is used, and the photovoltaic power generation power prediction precision and interpretability are improved through pre-training and human feedback; and step 3, constructing a fusion model: fusing prediction output of the weather typing and photovoltaic output prediction model and the RLHF large model to obtain a final prediction result of the photovoltaic power generation power. The invention also discloses a photovoltaic power prediction system based on seasonal weather typing and the RLHF large model.

The invention discloses an lncRNA-miRNA association prediction method of a multi-channel attention mechanism, and relates to an lncRNA-miRNA association prediction method. The method aims to solve the problem that more effective and robust feature representation may not be learned due to the fact that feature representation of multiple data sources cannot be properly fused and similarity and difference between samples cannot be learned through a contrast learning method in an existing method. According to the method, multi-source data of lncRNA and miRNA is obtained, and multiple similarity matrixes and coding matrixes are obtained through calculation; in combination with a graph convolutional network and a Transform, capturing information from a local angle and a global angle; fusing feature representations of different data sources by using a multi-channel attention mechanism; a contrast learning method is introduced to further optimize feature representation, so that the consistency between different modal features of the same lncRNA or miRNA is ensured, and the discrimination capability of the model is enhanced; in the final prediction stage, KAN is utilized to obtain a prediction score associated with lncRNA-miRNA, and the parameter and calculation efficiency are further improved.

The invention relates to the technical field of building industry carbon emission prediction, in particular to a building industry carbon emission prediction method and system based on a random forest algorithm, and the method comprises the steps: obtaining building industry carbon emission historical data; based on a random forest regression model, carrying out influence factor importance degree sorting on historical data of carbon emission in the building industry, extracting a characteristic data set of carbon emission correlation, and dividing the characteristic data set into a training set and a test set; inputting the training set into a preset LSTM model for calculation, evaluating an LSTM prediction result through the test set, and outputting the trained LSTM model; and combining the random forest regression model with the completely trained LSTM model, and completing carbon emission prediction of the existing building through the combined model. The invention aims to improve the accuracy and reliability of building industry carbon emission prediction.

The invention discloses an equipment maintenance knowledge representation method combining knowledge distillation and semantic analysis. The method comprises the steps of collecting structured data, unstructured text data and multi-modal data, constructing a semantic knowledge graph by using a natural language processing technology, extracting visual knowledge by using a computer vision technology, and fusing the visual knowledge with semantic knowledge to form a multi-modal knowledge base. And performing interactive knowledge distillation on the text data by using a large language model, generating optimized knowledge key points, and fusing the optimized knowledge key points with the multi-modal knowledge base to generate a comprehensive knowledge base. Knowledge is hierarchically organized according to the domain ontology and stored in a graph database, a natural language query interface is constructed, intelligent answering is performed by integrating technologies, question and answer corpus training is automatically generated, and automatic question and answer services are provided for users. According to the method, heterogeneous data integration, multi-modal knowledge fusion, hierarchical knowledge organization, natural language interaction and man-machine collaborative optimization are realized, and an intelligent equipment maintenance knowledge base can be efficiently constructed.

The invention provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

The invention relates to the technical field of biological information mining, in particular to a multi-omics data optimization method and system for intestinal flora matching and a medium. The method comprises the following steps: acquiring historical intestinal tract multi-omics data and integrating the historical intestinal tract multi-omics data to obtain host intestinal tract multi-omics data; performing multi-omics data fusion based on the host intestinal multi-omics data to obtain microbial flora fusion data; performing pattern classification according to the host intestinal tract multi-omics data to obtain host intestinal tract physiological pattern data; carrying out physiological pattern influence factor calculation on the microbial flora fusion data to obtain host intestinal tract physiological pattern influence factors; and performing host intestinal flora matching on the microbial flora fusion data and the host intestinal physiological data according to the host intestinal physiological pattern influence factor, thereby obtaining intestinal flora matching data. According to the method, the accuracy of flora matching prediction can be improved.

The invention relates to the related technical field of digital data processing, in particular to an intelligent ancient book information processing method and device based on artificial intelligence, and the method comprises the steps: scanning an ancient book, constructing a knowledge graph, dividing a domain database, and training a vertical processing model; introducing a multi-form historical carrier to form a multi-mode sensing set, establishing a multi-information network, and embedding a processing model; the method comprises the following steps: establishing a dialog box, realizing multi-round interaction and combining threshold optimization output, solving the technical problems of digitization and fragmentation limitation in ancient book information processing and incapability of effectively integrating and utilizing ancient book information due to diversity and complexity of ancient books and literatures, and realizing construction of a knowledge graph and division of a domain database. A knowledge system with a hierarchical structure and logic association is formed, deep learning and optimization are performed according to specific requirements in the field of ancient books by training a vertical processing model, a dialog box is established, multi-round interactive feedback with the user is performed, and the user is assisted to quickly and deeply explore the ancient books.

The invention discloses an unmanned aerial vehicle image detection method and system fusing visible light and infrared light, and relates to a target detection technology, and the method comprises the steps: starting a visible light sensor and an infrared sensor in groups, and carrying out the unmanned aerial vehicle detection of a target region according to a preset detection strategy; feature extraction is performed on the detected infrared image and visible light image by using independent branches, and a Res-CBAM attention mechanism module is arranged between the two independent branches; carrying out feature fusion on the image features extracted by the two branches by adopting a BiFPN structure; and based on the fused image features, performing target detection based on the classification network and the regression network to output a classification result. The target feature information in the infrared features and the scene detail information in the visible light features are fused, the complementarity of the features is enhanced, and the comprehensive performance of the model is improved.

The invention relates to the field of diabetic feet, and discloses a diabetic foot risk assessment method and system based on a knowledge graph, and the method comprises the steps: collecting patient data of diabetic feet, recognizing patient group features, risk factors and risk features of the diabetic feet, constructing a risk prediction model of the diabetic feet, extracting keywords from risk prediction data of the risk prediction model so as to query domain knowledge of the diabetic foot, constructing a diabetic foot ontology structure, identifying an entity object of the ontology structure, performing disambiguation processing, extracting entity data of the entity object from the domain knowledge, and obtaining a disambiguation result of the entity object according to the disambiguation result and the entity data. The storage knowledge of the diabetic foot is fused, the reasoning conclusion of the diabetic foot is obtained by combining the reasoning rule and the reasoning type, the risk factor and the risk level of the patient data are recognized, and a risk assessment report of the diabetic foot is generated. The accuracy of diabetic foot risk assessment can be improved.

The invention relates to the technical field of temperature and humidity control, in particular to an intelligent temperature and humidity control method and system for a low-temperature fresh-keeping metal granary. The method comprises the following steps: firstly, acquiring various original data of the metal granary, predicting short-term environmental data in the granary in combination with a TFT model, and providing an accurate prediction basis for temperature and humidity control; secondly, multi-source data fusion is carried out on insect body monitoring images and audio frequency, image and audio frequency information is comprehensively analyzed, and the comprehensiveness and multi-dimensional analysis capability of insect pest monitoring are improved; finally, the accuracy and sensitivity of pest monitoring are improved through a multi-source data recognition model, a temperature and humidity control strategy is optimized in combination with a DQN reinforcement learning model, automatic and intelligent temperature and humidity adjustment is achieved, the environmental conditions in a granary are guaranteed, and the grain storage period is effectively prolonged.

The invention discloses an intelligent multi-sensor fusion board separator fault prediction and diagnosis method and system. The method comprises the steps of collecting data of various sensors on a board separator; carrying out random smooth secondary sampling preprocessing on the sensor data; establishing a causal relationship model by using the causal depth set of the separation strategy evaluation under the space-time interference, and carrying out the separation strategy learning of the sensor data; based on a result of the separation strategy learning, extracting features of the sensor data and generating causal sequence data; and based on the extracted features and the causal sequence data, applying a hybrid model to monitor the state of the board separator in real time, predicting potential faults, and generating maintenance suggestions.

The invention discloses an image super-resolution enhancement method for image segmentation, which combines super-resolution enhancement and image semantic segmentation by constructing a task-driven joint training framework, trains a segmentation network and a super-resolution enhancement network at the same time, and optimizes the super-resolution enhancement network through mutual collaborative optimization. The segmentation result and the super-resolution enhancement result are enhanced mutually, and image segmentation is completed. The method is different from an existing single training method, the overall performance of super-resolution enhancement and semantic segmentation tasks can be remarkably improved, the quality and semantic consistency of the enhanced image are improved by optimizing the loss functions of super-resolution enhancement and semantic segmentation at the same time and fully combining detail information and semantic information of the image, and the image quality is improved. By introducing a targeted loss function, high-frequency details and context information of the image are effectively integrated, the precision of semantic segmentation is improved while image enhancement is ensured, the explanation ability of the model is improved, and the robustness and adaptability of the model in a complex scene are also remarkably enhanced.

The invention, which relates to the automatic driving field, discloses a Transform-based networked automatic driving vehicle trajectory prediction method comprising the following steps: obtaining vehicle driving data and preprocessing the vehicle driving data to obtain time sequence information of each vehicle; a context vector of each vehicle is obtained by using a Transform encoder; inputting the context vector of each vehicle into a graph neural network to obtain an overall feature context vector I; obtaining a driving intention probability matrix of the current vehicle by using a random forest algorithm; splicing the driving intention probability matrix of the current vehicle and the overall feature context vector I, inputting the spliced driving intention probability matrix and the spliced overall feature context vector I into a decoder, and finally outputting the trajectory distribution probability of the vehicle; through combination of Transformer and GNN, not only can the dynamic state of a single vehicle be understood, but also the relative position and motion relation between vehicles can be understood, so that the vehicle trajectory can be predicted more accurately.

The invention discloses a very simplified Transform method for brain network learning and classification, which comprises the following steps of: (1) carrying out the following preprocessing on resting state functional magnetic resonance imaging signal data acquired by functional magnetic resonance equipment by using a DPARSF toolbox: (2) carrying out the following preprocessing on the resting state functional magnetic resonance imaging signal data acquired by the functional magnetic resonance equipment by using a DPARSF toolbox; and (3) based on the extracted time sequence of each brain region, inputting a Miniformer model, carrying out a learning process of the brain function network, and then entering a classification process of the brain function network. In addition, two variants of Miniform are designed, regular term constraints of sparsity and time smoothness are introduced respectively, and it is verified that the proposed architecture can flexibly integrate prior information. According to the method, the number of parameters needing to be learned is reduced, training based on limited data is more effective, and the interpretability of model parameters and a final result is enhanced.

The invention relates to the technical field of data mining, in particular to a pediatric clinical intelligent medical decision model training method. The method comprises the following steps: acquiring historical pediatric clinical data and desensitizing to obtain historical pediatric clinical desensitization data; performing data fusion according to the historical pediatric clinical desensitization data to obtain historical pediatric clinical fusion data; performing pediatric health care detection on the historical pediatric clinical fusion data to obtain abnormal pediatric health care data; calculating health recovery influence factors of the abnormal pediatric health care data to obtain the health recovery influence factors; performing pediatric doctor recommendation priority division on the historical pediatric clinical sensitivity reduction data to obtain pediatric doctor recommendation priority data; and according to the pediatric doctor recommendation priority data and the health recovery influence factors, constructing an intelligent pediatric medical decision model. The decision-making efficiency of pediatric medicine can be improved.

The invention discloses an automobile software defect prediction method based on dynamic feature selection and oversampling, which comprises the following steps: acquiring a data set of automobile software defects, and defining a full feature set of the data set; performing data preprocessing on the data set, and dividing the data set into a training set and a test set; dynamically evaluating the contribution of each feature in the full feature set to a decision feature through a granularity decision entropy algorithm, and reserving important features with high information gain; new samples are generated for the minority class samples by introducing an intelligent resampling algorithm of auxiliary loss and diversity loss, and the representativeness and diversity of the minority class samples are enhanced; training the defect prediction model through the training set and testing the test set; and outputting a corresponding defect prediction result based on the characteristic data of the automobile software through the trained defect prediction model. According to the method, the accuracy of automobile software defect prediction and the generalization ability of the model in new data or multi-class and mixed type data environments can be improved.

The invention provides a scene management and intelligent recommendation method and system based on a data space, and the method comprises the steps: selecting a system function in a preset database, and carrying out the matching of a corresponding data type according to the selected system function, making a corresponding data acquisition plan according to system functions and data types; collecting data based on the formulated data collection plan and selecting a data processing model matched with the collected data according to the collected data; processing the collected data to form a feature fusion layer, and introducing an attention mechanism and an inter-channel information transmission mechanism into the selected data processing model to optimize the selected data processing model; and training the optimized data processing model to obtain an attention mechanism weight matrix, and adjusting the obtained attention weight matrix to realize personalized recommendation. The technical problem that the heterogeneous data processing and recommendation ability is insufficient in the related technology is solved.

The invention discloses a drainage basin unit debris flow susceptibility evaluation method based on multi-model Stacking integration. The method comprises the following steps: constructing a debris flow disaster data set; constructing a Stacking ensemble learning model which comprises four base models, a feature stacking layer, an attention layer, a mixed feature layer, a feature splicing layer and a meta-model; a Stacking integrated learning model is trained; predicting the debris flow disaster probability of the drainage basin unit to be identified; and performing debris flow susceptibility evaluation. According to the method, multi-source data can be efficiently fused, and the mixed probability vector and the original sample are creatively spliced, so that the meta-model can simultaneously utilize the intrinsic characteristics of the original data and the prediction information of the base model in the training process; interference of irrelevant information and dependence on the quality of a base model can be reduced, key information in data can be captured more comprehensively, and the explanation capacity and prediction precision of the model on complex data are enhanced.

The invention belongs to the technical field of biological medicine and data processing, and particularly relates to a multi-modal-based liver transplantation drug dosage prediction method, which comprises the following steps: collecting clinical data of liver transplantation recipients and processing to obtain a sample set, and dividing the sample set into a training set and a test set; constructing a multi-modal linear regression model; samples in the training set pass through a feature extraction module to obtain an original feature sequence; inputting the original feature sequence into a feature dimension reduction module to obtain a dimension reduction feature sequence; inputting the dimension reduction feature sequence into an attention mechanism module to obtain an enhanced feature sequence; the enhanced feature sequence passes through a multi-modal fusion module to obtain a fused feature sequence; inputting the fused feature sequence into a prediction module to obtain a prediction result, and training optimization model parameters; obtaining to-be-processed clinical data, inputting the to-be-processed clinical data into the trained multi-modal linear regression model, and obtaining a medicament dosage prediction result; according to the method, the individualized dosage of tacrolimus can be accurately predicted.

The invention relates to the field of artificial intelligence, and discloses a text processing method and device based on a hybrid expert model, equipment and a medium, and the method comprises the following steps: receiving a to-be-processed text input by a user; matching the to-be-processed text with professional fields corresponding to n expert models in the mixed expert model to generate a field matching result; m expert models are activated in the n expert models according to the field matching result and a preset activation strategy, the weights of the m currently activated expert models are configured, and m is smaller than or equal to n; and inputting a to-be-processed text into the currently activated m expert models for text processing, and then generating a corresponding final output text. The text is matched with the professional fields of different expert models, the corresponding expert model is selected and activated based on the matching result, and the weight is configured, so that the field where the activated expert model is skilled is matched with the text, the interpretation ability of the hybrid expert model in the professional fields is improved, and the interpretation efficiency of the hybrid expert model is improved. And the operation efficiency and the output accuracy of the model are improved.

The invention is applicable to the field of near-infrared spectrum modeling analysis, and provides a near-infrared spectrum modeling characteristic mode generation and collaborative optimization method, which comprises the following steps: acquiring sample near-infrared spectrum data, performing preprocessing operation, and converting one-dimensional near-infrared spectrum data into two-dimensional structure data by using a conversion method; on the basis of the existing two-dimensional structure data, generating more two-dimensional structure data by utilizing a generation technology; for different feature modes, extracting depth features by using different neural networks, and constructing an optimal minimum feature mode set by using a cross adaptive attention mechanism and a feature collaborative construction method; and building a model according to the optimal minimum characteristic mode set, optimizing the model by using a collaborative optimization loss function, and finally realizing efficient modeling and accurate analysis of the near infrared spectrum data. According to the method, the accuracy of near infrared spectrum modeling in substance component content analysis is remarkably improved.

The invention discloses application of a boiler side main auxiliary machine AI intelligent early warning and multi-dimensional monitoring platform. Belongs to the technical field of boiler side auxiliary machine monitoring, and in the boiler side auxiliary machine monitoring system, a model training sub-module can learn complex modes and relations from historical data by adopting advanced machine learning algorithms such as a support vector machine, so that accurate prediction of the equipment state is realized. The self-learning and adjusting capabilities of the model enable the model to adapt to changes in equipment operation, and the prediction accuracy is kept. In addition, the continuous optimization of the model training sub-module is beneficial to improving the operation efficiency and reliability of the equipment, and powerful support is provided for equipment maintenance and fault prevention. The model evaluation sub-module ensures that the performance of the model reaches an expected standard through evaluation technologies such as cross validation and the like, and prevents an over-fitting phenomenon from occurring. The evaluation not only improves the reliability of the model in practical application, but also ensures that the model has good generalization ability when facing unknown data.

The invention discloses a sailor workload adaptive prediction method based on multi-source heterogeneous data fusion. The method comprises the following steps: acquiring physiological feature data of sailors to obtain the current physiological load of an individual; obtaining an individual physiological load prediction value in the next time period based on the current physiological load of the individual and the physiological load parameters at the historical moment; acquiring the navigation environment data of the ship where the crew is located in the next time period, and obtaining the comprehensive pressure level of the navigation environment where the crew is located in the next time period; and inputting a causal reasoning graph model based on the comprehensive pressure level of the sailing environment in which the sailors are located in the next time period and the individual physiological load prediction value in the next time period, and predicting the workload level of the sailors. According to the method, the physiological feature data, the historical physiological load parameters and the navigation environment data of the sailors are collected and fused, the working states of the sailors are comprehensively reflected in multiple dimensions, and the information amount and the comprehensive analysis capacity of the prediction model are improved.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.