1183 results about "Emotion identification" patented technology

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention provides a system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention provides a dynamic calibration method and system for a vehicle-mounted emotion recognition system, and the method comprises the steps: S1, obtaining multi-source data which comprises a facial image, a voice signal and a physiological signal; the obtained multi-source data are preprocessed, and preprocessed multi-source data are obtained; s2, performing feature extraction based on the preprocessed facial image, the voice signal and the physiological signal to obtain a facial expression feature vector, an audio feature vector and a physiological state feature vector; s3, evaluating the current environment credibility based on an environment credibility evaluation function; s4, dynamically distributing the weight of the multi-source data according to the credibility of the current environment and the real-time scene; and S5, constructing a multi-modal fusion vector based on the dynamically distributed weight of the multi-source data, the facial expression feature vector, the audio feature vector and the physiological state feature vector, and performing emotion recognition by using the constructed emotion recognition model based on the multi-modal fusion vector.

The invention discloses a multi-modal signal fusion emotion recognition method based on an attention mechanism. The method comprises the following steps: firstly, preprocessing physiological signal data; constructing a channel attention module for the multi-channel electroencephalogram data; respectively extracting EEG and other physiological signal features by using the EEGNet; a shared-private encoder is introduced to decouple shared features among the multiple modes and private features of each mode; and finally, providing a cross-modal cross attention fusion mechanism to realize multi-modal feature interaction and effective fusion. According to the method, multi-modal signals are combined, the limitation of a single mode is overcome, and a sharing-private feature separation mechanism and a cross-modal cross fusion mechanism are introduced, so that modal collaborative modeling and effective integration are realized, and the accuracy and generalization ability of emotion recognition are improved.

A dog training method for intelligently correcting pet behaviors based on multi-modality comprises the steps that multi-modality perception data of a pet is obtained, the multi-modality perception data comprises acoustic signals, motion signals and track data, an acoustic feature tensor, a motion feature tensor and a position feature tensor are constructed, and a unified coupling tensor is formed; inputting the coupling tensor into a pre-trained behavior recognition model, and outputting a behavior tag; the emotion recognition module outputs an emotion label, and updates a character label in a sliding window mode; inputting the behavior label, the emotion label and the character label into an intervention strategy generation model and outputting an adaptive intervention strategy, wherein the intervention strategy comprises a pacifying type strategy and a stimulation type strategy; according to a historical feedback effect record, adjusting a feedback intensity parameter of the intervention strategy, calculating a feedback convergence index and an emotion recovery index, and dynamically correcting a feedback level and a delay strategy of the intervention strategy.

The invention provides a self-learning multi-modal emotion recognition method based on multi-scale cavity attention, and solves the problem of low recognition precision caused by different importance of basic action units of a face and different distances between key action units, and the problem of different modality confidence during decision-level fusion. The method comprises the following steps: preprocessing a facial expression image, inputting the facial expression image into a multi-scale cavity attention convolution module, extracting features through a parallel three-branch convolution structure, splicing the features, calibrating through an attention mechanism to obtain an enhanced feature map, and sending the enhanced feature map to a full connection layer to recognize emotion; an original electroencephalogram signal is input into a time-frequency-space three-dimensional feature extraction network, the signal is decomposed, differential entropy features are calculated and processed by a global attention module comprising a frequency spectrum attention module, a space attention module and a time attention module, time-frequency-space multi-dimensional feature representation is output, and emotions are recognized by a full connection layer; and finally, inputting the emotion recognition result of the facial expression and the electroencephalogram signal into a self-learning weight module, and obtaining a final emotion recognition result through dynamic weighted fusion.

The invention relates to the technical field of pet emotion recognition, in particular to a pet dog emotion recognition method and system based on deep learning. Comprising the steps of collecting pet dynamic data, pet physiological data and scene data to obtain a structured data set; labeling the structured data set through a cross validation labeling mechanism to obtain a labeled data set; multi-modal features are extracted based on the labeled data set, and multi-modal feature integration is carried out through a cascade SEblock array to obtain multi-modal fusion features; adversarial sample data generated by a stress scene simulator is injected in a training stage, and deep learning model training is performed based on the adversarial sample data and the multi-modal fusion features to obtain a pet emotion recognition model; and performing emotion recognition on a to-be-recognized pet through the pet emotion recognition model to obtain a pet emotion recognition result. According to the method, the data quality and the model robustness of pet emotion recognition are improved, and then the human-pet interaction quality is improved.

The invention discloses an emotion recognition method based on a space-time multi-scale attention convolutional neural network, and the method comprises the steps: collecting electroencephalogram data of a subject, and carrying out the preprocessing of the electroencephalogram data, so as to obtain electroencephalogram (EEG) data containing a space dimension and a time dimension; constructing a lightweight convolutional neural network comprising a double-flow spatial-temporal feature construction layer, a mixed attention mechanism layer, a high-order fusion layer and a classification layer; wherein the double-flow spatial-temporal feature construction layer comprises a temporal feature extraction module and a parallel spatial feature extraction module; the mixed attention mechanism layer combines a channel attention mechanism, a space attention mechanism and a self-attention mechanism; the high-order fusion layer is used for carrying out re-learning from the learned global convolution kernel to the representation of the local hemisphere convolution kernel; and recognizing the EEG data by using the trained lightweight convolutional neural network to obtain an emotion recognition result of the subject. The precision and efficiency of emotion recognition driven by electroencephalogram signals are improved by constructing a lightweight model with a small number of parameters.

The embodiment of the invention provides a vehicle-mounted emotion interaction method and device based on multi-dimensional recognition, and the method and device achieve the precise judgment of the emotion of a driver through innovatively constructing an emotion fusion recognition model and integrating the facial expression, voice emotion, driving behavior and physiological state features. And designing a scene-based self-adaptive interaction strategy, and establishing an interaction triggering threshold value for intelligent matching in combination with external environment data and a danger level. An interaction effect evaluation mechanism is introduced, an interaction strategy model is continuously optimized through an online learning module, and dynamic adjustment of personalized interaction content is achieved. According to the method, the defects of the traditional technology in the aspects of emotion recognition, interaction strategies, effect evaluation and the like are effectively overcome, and the intelligent level and the user experience of vehicle-mounted emotion interaction are remarkably improved.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The embodiment of the invention provides an emotion recognition method and device based on artificial intelligence, and identity verification is realized through voiceprint features by creatively integrating multi-modal data of facial images, voices and body actions. And a multi-level attention fusion network is designed, and intelligent fusion of modal interior and cross-modal features is realized by using a feature attention layer and a modal attention layer. An emotion change time sequence model is constructed in combination with medical record data, the physiological indexes are fused for evaluation and correction, and dynamic evaluation and accurate monitoring of the emotional state are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal fusion, time sequence modeling, medical monitoring and the like are effectively overcome, and the accuracy and the practical value of emotion recognition in the medical scene are remarkably improved.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

The invention relates to the technical field of emotion recognition, in particular to an electroencephalogram emotion recognition method based on an attention mechanism and multi-feature fusion, which comprises the following steps: acquiring electroencephalogram signal data, preprocessing the electroencephalogram signal data, and obtaining an electroencephalogram emotion recognition result through a constructed TSS-ENet network model; and carrying out time domain, frequency domain and space domain feature extraction and fusion on the preprocessed electroencephalogram signal data, and finally obtaining an emotion classification result of the electroencephalogram signal data. According to the method, features are directly extracted from electroencephalogram signal data in an end-to-end manner, and electroencephalogram signal emotion classification is adaptively performed in combination with the TSS-ENet network model, so that the accuracy of emotion recognition is effectively improved.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention discloses an emotion prediction and disease derivation method and system based on multi-modal fusion, and the system comprises a data collection and preprocessing module, an emotion fusion module, an abnormal condition detection and cloud uploading module, and a disease possibility derivation module. The data acquisition and preprocessing module comprises a video part, a text part and an audio part, and the video part comprises face emotion recognition and prediction and human motion recognition and prediction; the text part comprises text content emotion recognition and prediction; the audio part comprises voice-to-text and voice tone emotion recognition and prediction, the system comprehensively captures an emotion state by fusing multi-mode information such as video, text and voice, and the accuracy and prediction capability of emotion recognition are improved; and by predicting the future emotion trend, the abnormal condition is warned in advance, and the response timeliness is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a call service quality auditing method, device, equipment and medium, and the method comprises the steps: collecting call audio data, and translating the call audio data into call text data; identifying a client intention in the call text data by using an intention identification model, and generating a client intention result; using an emotion recognition model to recognize customer emotion, and generating an emotion recognition result; using the service response analysis model to identify service behavior performance, and generating a service response analysis result; and generating an audit report containing service quality analysis content based on the client intention result, the emotion recognition result and the service response analysis result. According to the method, the client intention, the emotional state and the service response behavior in the call content are processed in a unified manner, the unstructured call information is converted into the structured evaluation data, automatic, multi-dimensional and deep auditing analysis of the service quality is realized, and the auditing efficiency and accuracy are improved.

The invention relates to the field of data processing, and particularly discloses a user emotion recognition method based on AI and voice data, which obtains passive data of a user through natural language dialogue or voice interaction, and dynamically analyzes an emotion state in combination with an emotion recognition model. When the model detects that the emotion does not reach a discrimination threshold value, three levels of emotion induction mechanisms are triggered in sequence: in the primary stage, basic emotion data are supplemented by adopting a general guide verbal skill; in the intermediate stage, a personalized induction strategy is adapted based on user portrait features; and in the advanced stage, multi-modal auxiliary analysis is started. According to the technology, recessive emotions are effectively mined through a progressive data enhancement strategy, the proportion of effective emotion samples in customer service, psychological counseling and other scenes is increased, the emotion recognition accuracy is improved through a three-layer data compensation mechanism especially for the scene that a user actively hides the emotions, and deep emotion interaction and accurate recognition are achieved.

The invention discloses a classroom emotion recognition method based on a multi-view graph neural network, and relates to the technical field of deep learning and emotion recognition, and the method comprises the steps: respectively constructing a visual modal graph, a voice modal graph and a text modal graph based on visual features, voice features and text features; video segments are used as nodes in each modal diagram, and visual features, voice features and text features corresponding to each video segment are respectively used as node features of the corresponding modal diagram; respectively carrying out GAT coding on each modal diagram by utilizing a diagram attention network, and respectively updating node features in each modal diagram to obtain each updated modal diagram; performing multi-modal adaptive fusion on the basis of the updated modal diagrams to obtain multi-modal adaptive fusion features; and predicting the classroom emotion of the student by using the multi-modal adaptive fusion features. According to the method, the multi-view features are constructed by using the graph neural network, and the classroom emotions of the students are identified more comprehensively and accurately in combination with a multi-view feature fusion technology.

The invention relates to the technical field of voice signal processing, and discloses a voice emotion recognition method, device and equipment and a readable storage medium, and the voice emotion recognition method comprises the steps: obtaining a target voice signal, and executing sampling processing to obtain voice sampling data; extracting a plurality of acoustic features based on the voice sampling data, and constructing initial feature representation; in combination with environment signal-to-noise ratio information, performing channel weighted fusion processing to generate fusion feature representation; inputting the fusion feature representation into a time sequence modeling network, and extracting context information to obtain time sequence abstract features; and executing emotion recognition processing based on the time sequence abstract features to generate a corresponding emotion recognition result. According to the method, the emotion recognition accuracy in a multi-noise environment is improved, the expression ability of fine-grained emotion features such as speech speed changes and rhythm fluctuations is enhanced, the dynamic adaptability to environment changes in the feature fusion process is achieved, and higher recognition stability and environment adaptability are achieved.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention provides a Mama-based multi-modal dialogue emotion recognition method and system, and belongs to the field of dialogue emotion recognition. The problems that an existing multi-mode emotion recognition method is limited in memory ability, information is not fully utilized, and noise accumulation exists in long sequence fusion are solved. Comprising the following steps: processing dialogue data in different modes by adopting different preprocessing modes to obtain corresponding feature codes; performing convolution and information embedding on different feature codes; external attention is adopted to capture semantic information in each modal; interaction among different modes is realized by using a cross fusion mechanism; filtering noise in each mode by adopting Kalman filtering and establishing a relation between the modes; fusing the semantic information of different modals by using grouping pooling and cross attention, and then fusing the fused semantic information with a Kalman filtering result to obtain a fusion result; mapping the fusion result into a predefined emotion category; the method and the device are applied to multi-mode dialogue emotion recognition.

The invention provides a cognitive impairment assessment system and method based on emotion interaction multi-mode physiological signals. The cognitive impairment assessment system comprises a multi-mode physiological signal acquisition module, an emotion interaction module based on emotion recognition and a cognitive impairment assessment module. The multi-modal physiological signal acquisition module acquires multi-dimensional physiological signals such as electroencephalogram, electro-oculogram and electrocardio through a flexible sensor, and a physiological signal coupling network is constructed; the emotion interaction module based on emotion recognition adopts a heterogeneous graph neural network to analyze emotion response under visual stimulation, recognizes deviation between emotion and a preset stimulation type through a comparison model, dynamically optimizes visual stimulation parameters, and forms closed-loop emotion interaction; and the cognitive impairment evaluation module combines an SE-CNN deep learning model and fuses multi-modal physiological data and emotion interaction characteristics to realize accurate classification of cognitive impairment. According to the method, the accuracy and individualized efficiency of emotion recognition can be effectively improved, and a new normal form and a new method are provided for early cognitive impairment assessment.

The invention discloses a multi-scale emotion recognition method based on a DBBCapsNe model, and relates to the technical field of electroencephalogram signal processing and emotion recognition. According to the method, the diversified branch modules, the depth separable convolution, the SEBlock channel attention mechanism and the capsule network are combined, and efficient feature extraction and emotion classification of the electroencephalogram signals are achieved. According to the method, through an end-to-end model architecture, the whole network is divided into a time feature extractor and a capsule network, the time feature extractor module effectively extracts time domain features through a diversified branch module and depth separable convolution, and SEBlock is integrated, so that weight connection between channels is enhanced, the feature expression ability is further improved, and the time domain feature extraction efficiency is improved. And inputting the extracted feature information into the capsule network to extract spatial domain features and effectively integrate local and global relationships.

The invention provides an intelligent customer service dynamic intention recognition system based on a semantic analysis model, and the system collects the multi-dimensional data of a user through a data collection module, constructs a user portrait through a feature extraction module, extracts the portrait features, carries out the semantic analysis of multiple rounds of historical dialogue data, and extracts preference features. And constructing a dynamic user-entity association graph to extract GNN node features. Multi-modal data of a user is analyzed through an emotion recognition module, emotion features are recognized, portrait features, preference features and emotion features are fused and analyzed based on an MLP model to obtain a final emotion state, and the portrait features, the preference features, GNN node features and the emotion features are fused through an intention recognition module to obtain a final emotion state. According to the method, dynamic intention analysis is carried out on the basis of the Transform sequence-to-sequence model, the current intention of the user is recognized, the recognition accuracy is high, the dynamically changing intention is adjusted in real time, and more accurate and targeted answers or services are provided for the user.