951 results about "Sentiment analysis" patented technology

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention discloses a cross-cultural customer service dialogue quality automatic evaluation method in combination with sentiment analysis, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the alignment of voice and text based on a transmission matrix in real time, extracting a speech, a metaphor and polarity, and generating a speech tag; constructing an emotion channel and a polite channel, and fusing expression and shielding intensity through sharing attention; comparing and aligning with the same language prototype in a regional culture baseline library to obtain a calibration representation and updating a language offset record table; the potential upgrading probability is represented and recurred according to round aggregation calibration, and a risk vector and a high-risk position are formed; fusing risk and business indexes by a capacity integral kernel, outputting a comprehensive quality score, and giving factors and round attributions; sample recovery is triggered according to score and feedback difference, a micro-weight training data set is constructed, gradient increment training is carried out under low-rank adaptation, and cross-language consistency, early recognition of upgrading risks and interpretable evaluation are achieved through a closed loop.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention relates to the technical field of customer service quality inspection, in particular to an online customer service intelligent quality inspection system and method based on artificial intelligence. And automatically extracting a user demand keyword, an emotion expression keyword and a potential violation term keyword, and generating a structured keyword sequence. Performing emotion analysis on each dialogue round through a Transform model, and accurately outputting a customer emotion classification and an intensity score; and semantic correlation of the context is carried out through a neural network model. And automatically identifying the content of each round of dialogue and counting illegal verbal skills. Furthermore, the customer emotion value, the context coherence score and the occurrence frequency of violation verbal skills are input into a quality inspection scoring model, a comprehensive quality inspection score is automatically calculated, and whether the service is qualified or not is judged according to the comprehensive quality inspection score, so that the dynamic evaluation of the service quality is realized, the quality inspection efficiency is improved, and the customer experience is truly reflected.

The invention discloses a sentiment analysis method based on prototype guide mode fusion and prompt enhancement, and constructs a multi-mode sentiment analysis network which comprises a multi-mode coding module, a prototype guide mode fusion module, a dynamic mode weight adjustment mechanism and a context prompt generation module. The method comprises the following steps: firstly, extracting semantic features of each mode by using a multi-mode encoder, and constructing a prototype feature library based on a labeled sample to describe typical representations of different modes under each category; and then, dynamically evaluating modal contribution through prototype similarity to realize modal adaptive fusion. Furthermore, a context prompt is generated according to a similarity retrieval result of the input sample and the prototype library, and the pre-training language model is guided to complete sentiment classification. According to the method, the problems of modal inconsistency, information redundancy, weak small sample generalization and the like can be effectively relieved, and the accuracy and robustness of sentiment analysis are improved.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention discloses a space-time decoupling sentiment analysis method and system based on multi-modal data, and relates to the technical field of multi-modal sentiment analys.The method comprises the steps that text, audio and video initial features are input into a space-time decoupling and language focusing fusion model to be processed, and corresponding feature extraction is conducted on the enhanced text, audio and video features; obtaining specific text, audio and video features; performing shared feature extraction on the enhanced text, audio and video features to obtain shared text, audio and video features; the specific text, audio and video features and the shared text, audio and video features are input into a language focusing attractor module for multi-level feature extraction, and low-level features, middle-level features and high-level features are obtained respectively; emotion prediction is carried out based on the low-level features, the middle-level features and the high-level features, emotion prediction results of the features of all the levels are fused, a final emotion prediction result is obtained, and decoupling and fusion in multi-modal emotion analysis are achieved.

The invention discloses a multi-modal emotion fusion analysis method and system, and the method comprises the steps: carrying out the feature extraction of multi-modal emotion data modal by modal through a feature extraction module, and generating a text original feature, an audio original feature and a visual original feature; performing cross-modal alignment interactive fusion on the original text features, the original audio features and the original visual features based on a unified semantic alignment module, and constructing collaborative fusion features; performing mode and channel double-layer dynamic fusion optimization by adopting a dynamic fusion regulation and control module according to the text original feature, the audio original feature, the visual original feature and the collaborative fusion feature, and determining a unified fusion feature; performing hierarchical residual semantic gating enhancement based on the unified fusion features according to a high-order semantic abstraction module to generate semantic enhancement features; and inputting the semantic enhancement features into an emotion prediction module, and outputting an emotion analysis result. Based on the above scheme, a more stable, accurate and reliable emotion recognition result can be provided.

The invention discloses a digital human interaction method and device based on multi-modal sentiment analysis and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-modal data of a user in real time through a multi-source sensor device; the multi-modal data comprises face video stream data, voice audio stream data and text dialogue data; calling data analysis engines corresponding to different modalities, and extracting corresponding modal feature sequences; according to the current interaction scene, the modal feature sequence and the historical dialogue context features are fused, and a comprehensive emotion evaluation result is generated; outputting a corresponding multi-modal response data packet based on the modal feature sequence through an interactive response engine corresponding to a comprehensive emotion evaluation result; and executing the multi-modal response data packet. And after feature fusion is carried out in combination with the current interaction scene, the generated response can more accurately fit the current emotion demand and communication context of the user, so that the digital human can be more easily fused into various scenes needing emotion interaction.

The invention discloses a text sentiment analysis method, system and equipment based on multi-granularity sentiment modeling and a medium, and the text sentiment analysis method comprises the following steps: obtaining a to-be-analyzed initial text, and carrying out standardized preprocessing on the initial text to obtain text data; performing coarse-grained sentiment analysis on the text data by using a chapter-level encoder to generate chapter-level sentiment tags; performing fine-grained sentiment analysis on the text data by using a sentence-level encoder to generate a sentence-level sentiment tag; performing local correction on the sentence-level emotion label based on the chapter-level emotion label by using a cross-layer attention mechanism to obtain an updated sentence-level emotion label; extracting entity features and attribute tags in the text data, and associating the entity features, the attribute tags and the updated sentence-level emotion tags to generate an emotion triple; and carrying out conflict analysis on the emotion triad to obtain a structured emotion label of the initial text. According to the invention, the context consistency and accuracy of the sentiment analysis result can be improved.

The invention relates to a customer service interaction method and system fusing AI digital employees and multi-agent decision, and the method comprises the steps: receiving a multi-mode interaction request from a user, converting the multi-mode interaction request into interaction text data in a unified format, and forwarding the interaction text data to a multi-agent decision unit. And performing intention recognition and sentiment analysis on the interactive text data through the multi-agent decision-making unit to obtain a recognition analysis result. And based on the identification analysis result, guiding the interaction process of the user in combination with the historical interaction content so as to determine the interaction task type, and feeding back the interaction task of the corresponding type to the AI digital employee. And in response to the interaction task, calling the AI digital employee to execute the interaction task according to the rules and knowledge in the knowledge base, and generating a task execution result. According to the task execution result and the recognition analysis result, reply content based on the multi-modal interaction request is generated, the reply content is fed back to the user side, and the flexibility and accuracy of the interaction process are improved.

A method comprising retrieving one or more requirements of knowledge to be extracted; generating a prompt corresponding to the one or more requirements; validating the prompt by executing a large language model using the prompt and evaluating the response predicted by the large language model; fine-tuning the large language model using validation data generated as a result of validating the prompt; and executing the fine-tuned large language model using a text corpus to analyze one or more item reviews and generate a pair of at least one entity and a respective relationship sentiment value for the entity.

The invention discloses a multi-modal sentiment analysis method based on a gating circulation hierarchical fusion network, which comprises the following steps: S1, multi-modal data preprocessing: collecting text, audio and video data, carrying out preprocessing and time sequence alignment, and constructing an annotation data set; s2, constructing a gating circulation hierarchical fusion network: designing a three-level network architecture comprising a modal feature extraction layer, a gating fusion layer and an emotion recognition layer; s3, model training and optimization: adopting an Adam optimizer, taking a cross entropy loss function as a target, carrying out iterative training on the labeled data set, and inhibiting overfitting through Dropout regularization and learning rate attenuation; s4, emotion analysis: inputting data acquired in real time into the trained model, analyzing the emotion state of the user in real time through a feature extraction layer, a gating fusion layer and an emotion recognition layer, and outputting an emotion analysis result; according to the method, information of different modes can be fully utilized in the sentiment analysis task, and the sentiment recognition accuracy is improved.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

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 belongs to the technical field of data analysis, and provides a questionnaire-based sentiment analysis method, system and equipment in order to solve the problem of inaccurate user sentiment analysis in the existing questionnaire. Using a pre-trained language model to extract a semantic vector of the topic text, and combining with the co-occurrence frequency of the multiple topic options to generate node-level local features; carrying out dynamic reasoning by adopting an improved graph neural network model, calculating a dynamic attention coefficient based on semantic similarity calculated by a topic text semantic vector and a jump probability between topics, and weighting and aggregating neighbor features, so as to obtain context-associated topic node features; the global emotion is calculated by using the PageRank thought, a final emotion analysis result is obtained, dynamic context association generated due to questionnaire jump logic is effectively captured, and the reliability of grasping the overall emotion venation of the user is 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 provides an automatic consumption tag analysis system and method based on multi-agent collaboration, and the system comprises a data processing agent which is used for collecting data from a social media platform and carrying out the data preprocessing; the label identification intelligent agent is used for extracting consumption labels of different dimensions from the preprocessed data; the sentiment analysis agent is used for carrying out context modeling and sentiment tendency recognition and binding the recognized sentiment tendency to the corresponding consumption label; the label normalization agent is used for performing clustering and normalization processing on all consumption labels bound with emotional tendencies to generate a structured multi-layer label atlas; the central scheduling agent is used for scheduling other agents, generating a label analysis result by using the multi-layer label atlas and sending the label analysis result to the user; according to the invention, based on a multi-agent architecture, structured analysis is carried out on user tags, behavior attributes and consumption intentions in social media contents, so that high-precision and high-efficiency intelligent consumption insight is realized.

The invention provides an NLP-based customer service dialogue quality detection method and system, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining an e-commerce platform customer service dialogue text stream in real time; semantic understanding is conducted on the dialogue text flow, a semantic understanding result is output, and the semantic understanding result comprises a user consultation intention, key question elements and customer service inquiry information entity integrity; based on the semantic understanding result, performing sentiment analysis on the dialogue text stream, and outputting a structured sentiment analysis result; setting an initial state point and a termination state point in the dialogue processing path based on the semantic understanding result and the sentiment analysis result; the initial state point is a semantic vector fusing core problem elements fed back by the user for the first time and a current user emotional state value. According to the invention, through full-process intelligent processing, dynamic monitoring and accurate optimization of customer service quality are realized, and user satisfaction, customer service efficiency and business normalization are improved.

The invention discloses a multi-channel dynamic hypergraph sentiment analysis method and a multi-channel dynamic hypergraph sentiment analysis network fusing time sequence consistency, belongs to the field of artificial intelligence and multi-modal sentiment calculation, and aims to solve the problems existing in the existing sentiment analysis technology. The method comprises the following steps: S1, a multi-channel feature extraction step: extracting multi-channel features of a text mode and an audio mode through a heterogeneous pre-training model; s2, a local time sequence context fusion step based on a video number: fusing short-term emotional fluctuation based on a local context mechanism of the video number, and capturing long-range dependence across time dimensions through Transform; s3, a single-modal-multi-modal hypergraph collaborative prediction step: dynamically constructing a single-modal hypergraph and a multi-modal hypergraph in a training batch, and modeling a high-order relationship by adopting spectral domain-spatial domain hybrid convolution; and S4, a multi-level multi-branch supervision step: outputting a final emotion prediction result through joint optimization of an early MLP branch and a late hypergraph branch.

The invention relates to a speech recognition and transcription method and system based on multi-modal fusion and sentiment analysis, and relates to the field of speech recognizing.The speech recognition and transcription method comprises the steps that a target speech signal and auxiliary modal information of synchronous visual information and text context information are obtained firstly, and the speech signal is segmented and recognized to obtain speech feature vectors; the method comprises the following steps: extracting text context information to obtain a text auxiliary feature vector, carrying out multi-modal fusion on the text auxiliary feature vector and the text auxiliary feature vector to generate fusion feature representation so as to carry out voice transcription to obtain an initial transcription text, and carrying out sentiment analysis according to visual information and the initial transcription text to generate a sentiment feature tag; and finally, optimizing and correcting the initial transliteration text based on the label to obtain a target transliteration text, thereby solving the technical problems that the speech recognition transliteration is difficult to adapt to dialect diversity and the recognition accuracy and robustness are insufficient due to neglect of emotion information, and improving the recognition accuracy and robustness through fusion of multi-modal information and emotion analysis. The voice content can be recognized more accurately, the transcription text can be optimized, and the accuracy and quality of voice recognition transcription are improved.

The invention discloses a causal perception sentiment analysis method and system based on thinking chain reasoning, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring and reading a multi-modal sentiment analysis data set; extracting video features from the video data in the multi-modal sentiment analysis data set, including voice, text and visual modal features; using the training set and the test set to train and verify the causal perception emotion polarity alignment model; inputting the test set into the trained causal perception emotion polarity alignment model to obtain an emotion state prediction result; video features are input into a causal perception emotion polarity alignment model, and emotion clues are extracted through thinking chain prompt and a self-supervision verification mechanism; then performing causal intervention and anti-factual reasoning on each modal feature by using an emotion clue to obtain a causal-related single-modal feature; and finally, obtaining joint feature representation from the causal-related single-mode features through cross-mode interaction by using a multi-mode representation learning method, and predicting an emotional state.

The invention provides a multi-modal sentiment analysis method and system based on main modal two-stage guidance, and relates to the technical field of sentiment analysis. Inputting the multi-modal data into a multi-modal sentiment analysis model, and extracting language, visual and acoustic features from the multi-modal data through a feature extraction module; semantically decoupling the multi-modal features into modal invariant features and modal unique features through a feature space distribution alignment module, and realizing feature distribution alignment dominated by language modals through alignment reconstruction constraints; performing self-attention modeling on the modal invariant feature through an attention enhancement module to obtain a first enhanced feature, and adaptively enhancing the visual and acoustic unique features through a cross-modal attention mechanism by taking the language unique feature as a dominant feature to obtain a second enhanced feature; the first enhanced feature and the second enhanced feature are fused through the emotion prediction module, an emotion intensity prediction result is obtained through regression prediction, and the accuracy and robustness of emotion analysis in a complex scene are improved.

The invention discloses an intelligent terminal multi-mode sentiment analysis method and device and a server, and belongs to the field of computers. The method comprises the following steps: on an intelligent terminal side, acquiring multi-modal data such as texts, voices and physiological signals and extracting features; through intra-modal adaptive attention and cross-modal bidirectional attention calculation, a fusion feature vector is generated; the emotion category and intensity are determined based on the vector, and local updates including model parameter updates and knowledge sub-graphs are generated. The local updates are sent to a federated aggregation server that constructs a global knowledge graph based on updates received from a plurality of terminals and generates a global model. The terminal receives the global model to update the local model. The invention aims to solve the problems of privacy leakage risk, inaccurate multi-modal fusion and poor model adaptability in sentiment analysis in the prior art, and achieves the technical effects of remarkably improving sentiment analysis accuracy and personalized adaptability while protecting user privacy.

The invention discloses an'emotion-rhythm-vision 'triple dynamic alignment algorithm based on an AI multi-modal large language model, and relates to the technical field of cross-modal data processing. The method comprises the following steps: acquiring and preprocessing multi-modal data, synchronously acquiring audio, video and text data, and performing cleaning and timestamp standardization; cross-modal feature extraction: extracting text emotional semantics through an LLaMA-2 model, obtaining audio time sequence rhythm through a DTW algorithm, and extracting video visual features through a DenseNet model; based on dynamic space-time alignment of ST-CrossAttention, multi-modal features are fused, and weights are distributed; and generating an interpretable output and analysis report. The system comprises a multi-modal acquisition module, a feature extraction module, an alignment engine and an output module. According to the method, accurate alignment and emotion fusion of multi-modal data can be realized, the accuracy and interpretability of emotion analysis are improved, and the method is suitable for scenes such as movie and television analysis and human-computer interaction.

The invention relates to the field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method based on task association perception learning. According to the scheme, the method comprises the steps that multi-modal data including text, audio and visual modal data are obtained, feature extraction is conducted on the multi-modal data, a double-branch comparison module is constructed for each kind of modal data, each double-branch comparison module comprises a fusion branch and a comparison branch, and the fusion branch is connected with the comparison branch; input of a fusion branch and a comparison branch of each double-branch comparison module is an extracted text modal feature, an extracted audio modal feature and an extracted visual modal feature; and finally, feature fusion and prediction output are carried out, so that the accuracy and robustness of multi-modal sentiment analysis are improved. The method is suitable for multi-modal sentiment analysis.

The invention requests to protect an emotion analysis method based on a text-guided comparative learning and gating fusion mechanism, which is used for solving the problems of insufficient modal feature representation discrimination and insufficient modal information fusion in multi-modal emotion analysis. Firstly, features of three modes are extracted in a feature extraction module, then a text mode is selected as a dominant mode, a text-guided weighted comparison learning strategy is designed, the strategy screens positive and negative sample pairs through the text mode, and weights are dynamically distributed for hard pairs and easy pairs, so that the model keeps universal features while focusing on the hard pairs, and the model is optimized. Therefore, the discrimination capability of modal feature representation is improved. Secondly, in a feature fusion module, a text-guided gating fusion mechanism is provided, weights of text modals and cross-modal fusion features are adaptively adjusted through a gating unit, and a self-attention mechanism is used to suppress fusion noise, so that the modals are fused more fully. Experiments on public data sets CMU-MOSI and CMU-MOSEI show that compared with a reference method, the method provided by the invention has advanced performance on multiple indexes such as F1, ACC-2 and the like.

The invention belongs to the technical field of artificial intelligence, and discloses a CNN-based call quality inspection method, device and equipment and a storage medium, the CNN-based call quality inspection method comprises the following steps: inputting a word vector sequence obtained by converting a target text corresponding to a target seat call into a preset sentiment analysis model to obtain a sentiment tag corresponding to the target text; analyzing the importance degree of each word vector in the word vector sequence to obtain a keyword vector in the word vector sequence; performing named entity recognition on each word vector in the word vector sequence to obtain an optimal entity tag sequence corresponding to the word vector sequence; and splicing the audio feature, the emotion tag, the keyword vector and the optimal entity tag sequence, inputting the obtained multi-modal fusion feature into a preset evaluation model, and outputting to obtain a quality inspection result. The method and the system can be applied to business management systems of financial science and technology, medical health and the like, and solve the technical problem that efficiency and quality cannot be considered in a call quality inspection mode based on the prior art.