510 results about "Emotion classification" patented technology

The invention discloses a Transform-based cross-modal fusion multi-modal emotion recognition method and device, which are used for solving the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in a multi-modal emotion recognition task, and the method takes the accuracy and robustness of emotion recognition as performance evaluation indexes. Firstly, feature information of three modes of vision, voice and text is obtained, feature extraction is performed on each mode through a deep learning model, then features of different modes are fused by using a cross-mode Transform module, and a complex dependency relationship between the modes is dynamically modeled through a multi-head self-attention mechanism, so that more accurate emotion recognition is realized, and the emotion recognition efficiency is improved. And finally, performing emotion prediction on the fused features based on time sequence modeling and an emotion classification module. According to the method, the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in multi-modal emotion recognition can be effectively solved.

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention discloses a feature attention and bilinear gating fused speech emotion recognition method and device, and the method comprises the following steps: 1, collecting an audio file, obtaining corresponding label information, generating audio waveform and time frequency representation data through preprocessing, and constructing an audio waveform mask and a time frequency mask to mark an effective information region; 2, constructing a dual-path feature extraction module which comprises a time-frequency feature extraction module and a pre-training acoustic feature coding module; wherein the time-frequency feature extraction module models emotion correlation through local convolution and a multi-dimensional attention mechanism, and performs global time sequence modeling based on a bidirectional gated loop network; the pre-training acoustic feature coding module extracts high-level speech representation with high expression ability for emotion distinguishing by using a pre-training model; and step 3, constructing a feature fusion module and an emotion classification module, and combining with a dual-path feature extraction module to form a speech emotion recognition model.

The invention provides a text user sentiment analysis method and system based on multiple modes and AI, and relates to the technical field of text analysis, and the method comprises the steps: obtaining and preprocessing text, image and audio information of a user, and extracting semantic, visual and acoustic feature vectors; multi-modal features are fused through a cross-modal attention mechanism; utilizing a graph neural network to construct a user emotion social graph to calculate emotion propagation intensity; and finally, obtaining an analysis result containing emotion category and intensity through an emotion classifier. The emotion state of the user can be comprehensively captured, the emotion analysis accuracy is improved, and complex emotion expression is effectively recognized.

The invention relates to the technical field of robot perception, in particular to an intelligent accompanying human-type robot based on multi-mode emotion interaction and a perception method, and the method comprises the following steps: collecting facial expression and body posture data of a user through a visual perception unit, collecting intonation, speed, volume and keyword data through a voice perception unit, and carrying out the recognition of the speech, speed, volume and keyword data; data preprocessing and feature extraction are carried out; secondly, carrying out time sequence modeling on the extracted visual and voice features based on a recurrent neural network, and generating visual and voice time sequence feature vectors; the method comprises the steps that firstly, time sequence features are extracted, then, a multi-mode fusion model based on an attention mechanism is adopted, the time sequence features are fused into a comprehensive emotion feature vector used for emotion state classification, and finally, personalized interaction content such as voice, expressions and limb actions matched with the emotion state of a user is generated according to an emotion classification result. According to the method, the accuracy of robot emotion perception and the naturalness of man-machine interaction are remarkably improved, and the method is suitable for multi-scene application of education, medical treatment, old-age care and the like.

The invention relates to the technical field of language translation, in particular to a translation precision optimization method and system based on artificial intelligence, and the method comprises the steps: obtaining a source language text, carrying out the phrasing operation on the source language text to obtain a source language sentence sequence of the source text, carrying out the word segmentation operation on each source language sentence in the source language sentence sequence, and carrying out the word segmentation operation on each source language sentence; obtaining a word sequence of the source language sentence; and performing sentiment classification on each source language sentence in the source language sentence sequence based on a pre-trained first sentiment classification model. The sentiment classification is performed on the source language sentences and the target language sentences on the basis of the first sentiment classification model and the second sentiment classification model, and the similarity between the sentences is calculated, so that the sentences after translation are kept consistent in sentiment, the problem of sentiment deviation generated in a machine translation process is favorably avoided, and the translation efficiency is improved. Especially for some texts with strong emotion colors, it is ensured that translated emotion information can be correctly conveyed.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention discloses a video multi-mode sentiment analysis method and device based on multi-layer perceptron fusion, and relates to the technical field of sentiment analysis, and the method comprises the steps: S1, extracting text features, image features and audio features in a video; extracting time sequence information in the image features and the audio features to obtain time sequence image features and time sequence audio features; s2, constructing a video multi-modal sentiment analysis model comprising a multi-modal feature capture module, a multi-layer perceptron fusion module and a sentiment classifier, and constructing a loss function according to modal similarity and modal heterogeneity between modals; s3, training the model; and S4, inputting the text features, the time sequence image features and the time sequence audio features into the trained model to obtain emotion polarity probability distribution. According to the method, similarity loss and heterogeneity loss are constructed, and sequence, channel and modal dimension fusion is carried out by using a multi-layer perceptron, so that the calculation complexity and memory consumption are reduced, and the integrity and discrimination capability of the multi-modal emotion features are improved.

The invention relates to a multi-modal emotion recognition method based on heart and brain coupling and a graph neural network, and belongs to the field of artificial intelligence. Comprising the steps of data preprocessing, graph representation construction, multi-view graph convolutional network construction, fusion graph network construction and cross-domain joint optimization and sentiment classification. The method has the advantages that an adaptive adjacency matrix optimization strategy based on a triple constraint mechanism is proposed to solve the modal alignment and deviation problems represented by a multi-modal diagram in a data-driven branch, redundant noise is eliminated by adopting global regularization constraint, and unique feature representation in a modal is enhanced through modal specificity; a deep association rule is mined in combination with a cross-modal interaction module, the modeling ability of a heart and brain emotional state is improved, a multi-view image convolutional network is further designed, global features and local features are extracted, features of a cognitive heuristic branch and a data driven branch are combined by adopting an attention mechanism-based image fusion network, a domain confrontation strategy is introduced, and a cognitive network is constructed. And the generalization of the method is enhanced.

The invention provides a multi-modal emotion recognition software development method for voice interaction of an intelligent robot, and the method achieves the synchronous collection and fusion of multi-modal data through the weighted fusion preprocessing of voice signals, facial expression video streams and text contents. A DCNN and LSTM combined model is adopted to deeply extract emotional features, and an attention mechanism is introduced to strengthen key information capture. And a comprehensive emotion representation vector is calculated by using a cross-modal emotion association network CMAN, so that the emotion distinguishing capability is improved. An emotion classifier combining an SVM and a neural network is constructed, and refined output of emotion categories and intensity is achieved. Through real-time feedback and adaptive optimization steps, the model can continuously learn and adapt to user emotion expression habits, and accuracy and user satisfaction are improved. According to the invention, more humanized and accurate multi-modal emotion recognition technical support is provided for voice interaction of the intelligent robot.

Disclosed are a multi-mode emotion recognition method, a system, an electronic device, and a storage medium. The method includes obtaining a spectrogram of a voice to be recognized and a corresponding text and inputting the spectrogram and the text into a multi-mode emotion recognition model to obtain an emotion recognition result output by the multi-mode emotion recognition model. The multi-mode emotion recognition model is trained based on a sample spectrogram, and a corresponding sample text, and a sample emotion recognition result, and is configured to extract a feature from the spectrogram and the text by a self-attention mechanism to obtain the voice features and the text feature, fuse the text feature and voice feature to obtain a multi-mode fusion feature, and make an emotion classification decision to obtain an emotion recognition result based on the text feature, the voice feature, and the multi-mode fusion feature.

The invention discloses an emotion recognition method, system and device based on multi-modal adaptive fusion and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: selecting a pre-training model, respectively extracting the original features of an audio and a video, carrying out the preliminary extraction of the audio through a convolution layer, carrying out the multi-module processing of the video, and keeping the time sequence information. Constructing an attention module to generate an attention matrix and interaction features, and adjusting the original features by using the matrix; and inputting the weighted and fused features into a convolutional network to extract advanced time sequence features, performing pooling compression on the advanced time sequence features in a time dimension, splicing audio and video features, and finally sending the spliced audio and video features into a full-connection layer classifier to obtain an emotion classification result. According to the method, the weights of different features can be dynamically adjusted, so that the audio and visual features are effectively fused, the accuracy and robustness of emotion recognition are improved, the weighted recall rate and the unweighted recall rate are remarkably improved, and the method has high calculation efficiency and expandability.

The invention discloses a multi-modal emotion recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring text, video and audio data of a user and respectively performing feature extraction to obtain text features, audio features and facial features; the three features are input into a pre-trained multi-modal emotion recognition model, the multi-modal emotion recognition model comprises a first fusion module, a second fusion module, a third fusion module, a fourth fusion module and a classification module, the audio features and the text features are fused through the first fusion module, and audio text features are obtained; fusing the facial features and the text features by using a second fusion module to obtain facial text features; performing feature enhancement on the text features by using a third fusion module to obtain enhanced text features; fusing the audio text features, the face text features and the enhanced text features by using a fourth fusion module to obtain multi-modal features; and classifying the multi-modal features by using a classification module to obtain a sentiment classification result of the user.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a music stave sentiment classification method and system based on multi-level distillation, and belongs to the technical field of music information processing. The method comprises the steps of firstly collecting music stave data and converting the data into stave data vectors, then performing feature extraction by using a long short-term memory network, then constructing a teacher network and a student network for knowledge distillation, and realizing multi-level knowledge transmission through temperature scaling, KL divergence loss and mask feature distillation. And finally, training a lightweight classification model to complete sentiment classification. The knowledge distillation technology is creatively applied to staff sentiment classification, the classification accuracy is effectively improved through an online multi-level distillation mode, and the technical problems that a traditional method lacks semantic information and a self-supervised model is not suitable for sentiment tasks are solved. The method has the main advantages of high classification precision, light model weight, capability of effectively capturing music emotion features and the like.

The invention provides a multi-modal sentiment analysis method based on a diffusion model and self-paced learning. The method comprises the following steps: firstly, dividing a data set into a missing image modal data set and a complete modal data set according to image modal integrity; thirdly, constructing a feature alignment diffusion model, and performing image generation; training the diffusion model by adopting a self-paced learning strategy and a missing image data set; and based on the trained diffusion model, guiding a reverse process through text features to generate feature representation of the missing image. And carrying out weighted fusion on the generated image features and text features by using an attention mechanism, and dynamically adjusting contribution weights of all modalities to generate a complete multi-modal feature representation. And finally, integrating a missing modal completion result and the complete modal features to form a unified multi-modal representation, inputting the unified multi-modal representation into a multi-modal sentiment classification module, and outputting a sentiment classification result. According to the method, the problem of multi-modal sentiment analysis under random missing of image modals is effectively solved, and the generation quality and semantic consistency are improved.

The invention discloses a multi-modal image-text emotion recognition method and system based on a dynamic routing hybrid expert model, and aims to solve the technical problem that the model recognition effect is poor due to the fact that an existing multi-modal image-text emotion recognition method generally adopts a static fusion mechanism. The method comprises the steps that image data and text data are acquired, the image data and the text data are input into a preset multi-mode image-text emotion recognition network, and the preset multi-mode image-text emotion recognition network comprises a target encoder, a dynamic routing hybrid expert model and an emotion classifier; encoding the image data and the text data through a target encoder, and outputting image global features and text global features; performing multi-modal feature fusion on the image global features and the text global features to generate image text fusion features; performing dynamic expert calculation on the image text fusion features by adopting a dynamic routing hybrid expert model, and outputting weighted features; and inputting the weighted features into an emotion classifier to generate a target multi-modal image-text emotion recognition result.

The invention provides an event description text generation method, device and equipment based on video data, which can be applied to the field of computer vision. The method comprises the steps that video scene graph data is constructed based on to-be-processed video data, the video scene graph data comprises object nodes and edge relations, and the object nodes represent character objects in the to-be-processed video data and object objects related to the character objects; processing the to-be-processed video data based on the trained emotion classifier to obtain a target emotion attribute node, the target emotion attribute node representing the emotion attribute of the character object; updating the video scene graph data based on the target emotional attribute node to obtain a video knowledge graph, the video knowledge graph comprising an edge relationship between the object node and the target emotional attribute node; and processing the video knowledge graph by using the trained decoder to obtain a target event description text for describing the behavior of the character object.

The invention particularly relates to a multi-modal natural dialogue context sentiment analysis method based on personalized sentiment feature generation. The method comprises the steps of obtaining a training set; constructing a multi-modal sentiment analysis model for performing feature fusion through a cross-modal attention mechanism based on input visual, sound and text three-modal features, outputting a sentiment classification result, and calculating clustering center features of each sentiment type; generating personalized multi-modal features of the specific speaker by using the initialization information; training the multi-modal emotion analysis model by using the training set, performing parameter fine tuning on the model in the training stage by using the personalized multi-modal features of the specific speaker, and optimizing the emotion recognition ability for the specific speaker; and applying the fine-tuned multi-modal sentiment analysis model to the new dialogue data, and outputting a final sentiment classification result. According to the method, the personalized adaptive process related to the speaker is combined with the universal multi-modal sentiment analysis model, so that the overall recognition efficiency and precision are improved.

The invention discloses a multi-modal sentiment analysis method and system based on context enhancement and cross attention, and aims to solve the problem of low sentiment analysis precision caused by insufficient multi-modal feature fusion and insufficient context information utilization in the existing multi-modal sentiment analysis scheme. The system comprises a feature extraction module, an intra-modal context enhancement (ICE) module, a modal alignment and dynamic gating (GCU) weighting module, a time sequence-modal cross attention fusion (TMA) module (cross-modal depth fusion module), and a shared representation generation and multi-task parallel decoding module. The text features are subjected to deep context coding through a BERT model, and the audio and visual features are processed through multi-scale convolution and time sequence Transform; the ICE module enhances audio and visual features by capturing time sequence dependence in a single mode; the GCU module generates a gating weight moment by moment based on a GRU network driven by a global context, and dynamically weights the three modal features; the TMA module realizes deep interaction and fusion in time and modal dimensions through asymmetric time sequence-modal cross attention to generate a shared representation; and the decoding module executes sentiment regression, sentiment classification and modal reconstruction tasks in parallel. Through the structure, the precision and robustness of sentiment analysis in a complex scene are effectively improved.

The invention is suitable for the technical field of sentiment analysis, and particularly relates to a multi-modal sentiment analysis method based on pre-training and text modal guidance, and the method is implemented in two stages: the pre-training stage comprises the steps of obtaining initial features of each modal of multi-modal data, and decomposing each modal feature into similar features and different features; constructing positive and negative pairs in and between samples, calculating comparison loss and single-modal prediction errors, extracting module and projector parameters, and inputting a short video to obtain each modal feature; high-scale text representation is learned through initial text similar features, super-modal features are adaptively updated through a multi-head attention mechanism, and audio and video redundant information is suppressed by using a weight module guided by a text mode; through the data sampler and the feature comparison learning method, multi-mode fusion with the text mode as an anchor point is achieved, the emotional tendency in a complex scene can be recognized more accurately, and the method has wide value in the fields of emotion classification, social media content recommendation and the like.

The invention discloses a teaching speech emotion recognition method based on dynamic time sequence modeling and multi-scale fusion. The method comprises the steps of speech data set preprocessing, speech feature extraction, speech emotion classification network construction, speech emotion classification network training, inputting a test set into the trained speech emotion classification network, and outputting the probability of each type of emotion. According to the method, the direction of the information flow is dynamically adjusted through the adaptive time displacement module, the features of different time scales are extracted by using the multi-scale convolution branch, the modeling capability of the model for a complex time sequence structure and variation data is improved, the feature expression is enhanced, the time sequence features are extracted by using the Wav2Vec2.0 pre-training model, and the time sequence features are extracted by using the Wav2Vec2.0 pre-training model. A speech emotion classification network comprising an AdaShiftFormer learning module and a multi-scale time sequence fusion module is constructed, training is carried out in combination with classification loss and comparison loss, and classification performance is optimized. The method is superior to the prior art in emotion classification accuracy and feature expression ability, and can assist in teacher speech behavior analysis and classroom interaction optimization in an intelligent education scene.

The invention provides an aspect-level sentiment classification method and system based on multi-modal alignment and reflection enhancement, and relates to the technical field of artificial intelligence, and the method comprises the steps: employing a pre-trained teacher model to enable an original image to be combined with a knowledge guide prompt to generate text description; splicing the description, the original text, the target aspect word and the structured reasoning prompt template into a multi-modal aligned enhanced input sequence, and inputting the multi-modal aligned enhanced input sequence into a pre-trained student model to obtain a predicted emotion tag; aiming at a sample with a prediction error, generating an reflection-correction inference chain by utilizing a teacher model, and constructing an reflection enhancement data set; and finally, combining the original data set and the reflection enhancement data set, and performing supervised fine tuning training on the student model. According to the method, the semantic alignment and self-error correction capability of the model is enhanced, so that the sentiment classification accuracy is improved.

The invention relates to the cross technical field of artificial intelligence and multimedia communication, and discloses an intelligent optimization system and method fusing video resource scheduling and voice emotion recognition, and the system comprises a voice emotion recognition module which is used for collecting a user voice stream in real time, extracting acoustic features through a deep learning model, carrying out the emotion classification, and obtaining a voice emotion recognition result; and outputting the emotion label. According to the invention, through voice emotion recognition, the system realizes personalized resource allocation and content adjustment, so that the personalization and intelligence of user experience are greatly improved; the video resource scheduling module dynamically adjusts parameters according to a network, equipment and emotions to ensure smooth playing of a video and excellent image quality and tone quality; a multi-modal decision engine fuses a multivariate data generation strategy, resource utilization is optimized, and the emotion matching degree and the QoS score are improved; the intelligent optimization method is clear in process, video resources are accurately optimized, strategies are continuously updated through user feedback, the system adaptability is enhanced, and high-quality and personalized multimedia service experience is comprehensively provided for users.

The invention discloses a robot interaction system and method based on machine learning and emotion calculation, and the method comprises the steps: a robot collects the voice, facial expression, body language and physiological signal data of a user through a multi-mode perception device, and carries out the noise elimination, signal enhancement and feature extraction of the data; utilizing a deep neural network and an emotion classification algorithm to identify the emotion state of the user, identifying mixed emotion and performing label classification; a personalized emotion model is established through machine learning in combination with historical emotion data of the user, an emotion change rule is reflected, and long-term learning and adaptation are carried out; based on the sentiment analysis result, a response conforming to the user sentiment state is generated; through user feedback and interaction, the system continuously optimizes emotion understanding and reaction ability and updates emotion archives; emotion computing tasks are shared through edge computing or cloud computing resources, and real-time response to complex emotion computing tasks is ensured.

The invention relates to a social emotion analysis method and system based on multi-modal data. The method comprises the following steps: acquiring the multi-modal data; performing preliminary feature extraction on the preprocessed multi-modal data to generate a basic feature vector; performing second-level sentiment classification on the basic feature vector; performing deep analysis on a second-level emotion classification result to generate a fine-grained emotion map; according to the fine-grained emotion map, an emotion thermodynamic diagram is generated, the emotion thermodynamic diagram reflects emotion intensity through color depth, and the emotion thermodynamic diagram is displayed through a visual billboard. According to the method, efficient real-time analysis of multi-modal data is realized, complex emotion information can be quickly identified and displayed, and powerful support is provided for related decisions.

The invention provides a product comment sentiment analysis method based on MS-PSO and deep learning, and belongs to the field of sentiment analys.The method comprises the steps that a product comment text and sentiment classification are obtained, and text data are preprocessed; the preprocessed text is converted into a dynamic word level representation tensor through DistilBERT, and an emotion classification label is converted into an integer code; hyper-parameters of the mixed feature deep learning network are optimized and connected in series through a random mirroring particle swarm algorithm; according to the optimal hyper-parameter, training to obtain a parallel-series mixed feature deep learning network model; preprocessing text data to be subjected to sentiment classification, encoding the preprocessed text data through a DistilBERT model, and inputting the encoded text data into the trained model to obtain sentiment classification; according to the method, the product comment sentiment analysis precision can be improved, and a basis is provided for enterprises to know user requirements and improve products.

The present invention provides a user habit-based application (app) recommendation method, a mobile terminal, and a storage medium. The method comprises the steps of: collecting user inherent attribute information, app interaction information, and location information, performing analysis and quantification, then performing marking, and establishing user interaction scenario information; collecting input information during app interaction, and recording the input information as interaction associated data on the basis of a corresponding time period and an app package name; processing the interaction associated data via a first neural network, and outputting emotional classification; processing the interaction scenario information via a second neural network, and outputting real label probability distribution information; matching reference interaction scenarios in a corpus according to the real label probability distribution information, and finding corresponding apps; and performing emotional classification on the reference interaction scenarios of the apps on the basis of an SVM algorithm, and recommending to a user the app conforming to an emotional classification tendency. In this way, a user is helped to screen for the app fitting the app interaction habit of the user, and the time cost of searching for the app by the user is reduced.

The invention discloses an internal and external sentiment causal relationship analysis method, system, device and medium, and relates to the technical field of sentiment analysis, the method comprises the following steps: obtaining internal text sentiment data and external text sentiment data of a to-be-tested field; respectively extracting internal emotion features and external emotion features of the internal text emotion data and the external text emotion data, respectively clustering text emotions according to the internal emotion features and the external emotion features, and respectively converting the extracted emotion features into feature vectors; determining nodes in a directed graph model according to the internal emotion features, the external emotion features and the emotion classification result; determining an initial directed edge between the nodes according to the co-occurrence frequency of the emotional characteristics, and screening the directed edges of which the co-occurrence frequency is greater than a co-occurrence frequency threshold; constructing a directed graph model according to the determined nodes and the initial directed edge between the screened nodes; and analyzing the causal relationship between the internal emotion and the external emotion of the to-be-tested field through the directed graph model.

The invention relates to a multi-level sentiment classification method for network public opinions. According to the method, social media data are acquired and processed in real time through a distributed message queue, online clustering is performed by using a Streaming K-means algorithm, and an initial topic set is generated. A topic state space is constructed through a topic state analysis model, a proper classification level is selected by using a Q-learning algorithm, and an information entropy data set is constructed through dependency path analysis and information entropy calculation. And finally, generating a multi-level sentiment classification result through the level weight prediction model and the sentiment classification model, and using the multi-level sentiment classification result for network public opinion monitoring. The method can adapt to changes of network public opinions in real time, and the accuracy and timeliness of sentiment classification are improved.