446 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 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.

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 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 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 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.

The invention provides a multi-modal emotion fusion and user emotion intention understanding method and system based on an attention mechanism, and relates to the technical field of multi-modal emotion recognition. According to the method, firstly, a multi-modal dialogue data set is constructed by collecting voice, text, image and physiological data, and data preprocessing is carried out; secondly, extracting and coding multi-modal emotion features by using an improved convolutional neural network (CNN) and a long-short term memory (LSTM) network, and generating a feature set; then, a dynamic modal weighting mechanism is adopted, the weight of each modal feature is adjusted in real time according to the current dialogue situation, and a feature set is optimized through an adaptive noise suppression strategy and a generative adversarial network; and finally, carrying out feature fusion through a mixed cross entropy attention mechanism, inputting the fused features into an emotion classifier, generating a probability distribution vector of an emotion category, and identifying a user intention so as to generate interaction feedback in real time and display the interaction feedback. According to the method, the sentiment analysis accuracy and response flexibility are effectively improved.

The invention belongs to the technical field of sentiment analysis, and discloses a learning sentiment degree analysis method and system based on deep learning and a medium, and the method comprises the steps: obtaining learning interaction data of a user, the learning interaction data comprising text data, voice data and video data; performing feature extraction on the text data based on a semantic representation model to obtain text features; performing feature extraction on the voice data based on a time sequence model to obtain voice features; performing feature extraction on the video data based on a space-time sequence model to obtain visual features; performing multi-modal cross fusion on the text features, the voice features and the visual features to obtain multi-modal fusion features; and classifying the multi-modal fusion features based on a full connection layer of deep learning to obtain a sentiment classification result of the user. According to the invention, the overall performance of multi-modal sentiment analysis and the accuracy of sentiment analysis are remarkably improved.

The invention discloses a blogger live video AR glasses scenery labeling method and labeling system, and the method specifically comprises the steps: dynamically adjusting a knowledge graph retrieval strategy of a tourism live scene through a hot topic set and an emotion classification result, and obtaining a dynamic retrieval result; the method comprises the following steps: acquiring blogger head posture data based on an IMU (Inertial Measurement Unit) of AR (Augmented Reality) glasses, and constructing a 3D spatial topological map of a tourism live scene through an SLAM (Simultaneous Localization and Mapping) algorithm Spatial registration is carried out on the dynamic retrieval result and the 3D spatial topological map, and a scene enhancement labeling layer is generated through a NeRF algorithm; and displaying the video stream and the scene enhancement annotation layer in an overlapping manner, and dynamically adjusting the annotation visibility by adopting a self-adaptive transparency algorithm. According to the method, the video stream and the interaction data stream are collected in real time, dynamic annotation of the scene content of the AR glasses is achieved, live broadcast scene changes and audience interaction requirements can be responded in time, and the real-time performance and accuracy of live broadcast annotation are improved.

The invention belongs to the field of deep learning, and provides a cross-subject brain electrical emotion recognition method and system for space-time adaptive graph coding learning, and the method comprises the steps: extracting features from different frequency bands through a sliding window technology, and constructing a feature matrix covering channels and time dimensions; fusing space-time hybrid embedding, time embedding and space embedding, and converting the feature matrix into a high-dimensional semantic vector; calculating channel characteristic difference and time trend change, and generating a dynamic topology matrix adaptive to cross-tested individual difference; combining the dynamic topological matrix to calculate spatial cross-channel and time stride length attention in parallel, and fusing and strengthening key spatial-temporal features; and the decoder maps the coding features by means of a domain adversarial decoding module, reduces the distribution difference between a source domain and a target domain through cross-domain adversarial training, and outputs an emotion classification result. Through the method, the cross-subject generalization ability and the recognition accuracy of electroencephalogram emotion recognition are improved.

The invention discloses an emotion recognition and multi-dimensional teaching quality evaluation system and method based on artificial intelligence, and relates to the new technical field of artificial intelligence and teaching, and the system comprises an emotion recognition model which can efficiently extract and fuse emotion feature information in multi-dimensional peripheral physiological signals so as to realize accurate emotion classification. Based on the analysis results, the classroom teacher and student state analysis and teaching quality evaluation system constructs a classroom state analysis framework by quantifying student emotion, teacher emotion and classroom mutual dynamic characteristics, provides abundant and quantified classroom state data for educators, and generates multi-dimensional teaching quality evaluation indexes. According to the method, the teaching adjustment strategy can be dynamically generated and executed according to the evaluation result, and meanwhile, the structured teaching classroom state report is generated, so that the core problems of distraction of students, insufficient learning effect and the like in the current teaching scene can be effectively solved.

The invention discloses a Chinese language evaluation method and system based on character sentiment analysis, a medium and equipment, and the method comprises the steps: collecting sentiment labeling data, cultural background features and learning historical data of a user, and extracting sentiment primitive features and a sentiment combination mode through a multi-level sentiment classification model; quantizing the culture background features into culture cognition vectors and performing cross-modal alignment; utilizing a space-time attention mechanism to identify an emotion understanding breaking point and constructing a user cognition map; and finally generating a Chinese language ability report containing a multi-dimensional evaluation result. The method breaks through the quantitative limitation of traditional evaluation on emotion dimensions and cultural connotation, can accurately evaluate the dominant language ability of a learner by establishing a dynamic association model of emotion expression and cultural cognition, can deeply analyze the emotion understanding level and the cultural symbol mastering degree of the learner, and improves the evaluation accuracy of the learner. Accurate data support is provided for personalized language teaching, and scientificity and practicability of Chinese language ability evaluation are remarkably improved.