1058 results about "Sequence Feature" patented technology

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

The invention discloses an identification method and system of a short antibacterial peptide sequence, a terminal and a storage medium. Relates to the technical field of bioinformatics. A short antibacterial peptide data set is obtained in advance, a training set is obtained through division according to the short antibacterial peptide data set, and a pre-trained protein language model is finely adjusted according to the training set; obtaining sequence features of a to-be-classified sequence through the fine-tuned protein language model; obtaining physicochemical characteristics of the to-be-classified sequences through an analysis method based on pseudo amino acid components; inputting the sequence features and the physicochemical features into an attention model for feature fusion to obtain fusion features of the to-be-classified sequence; and inputting the fusion features into a classifier to obtain a prediction category of the to-be-classified sequence. According to the method, the pre-training model is finely adjusted by establishing a more perfect short antibacterial peptide data set, the sequence features and the physicochemical features of the to-be-classified sequences are fused, and the sequences are classified through the fused features, so that the recognition accuracy of the short antibacterial peptides can be effectively improved.

The invention provides a marketing path dynamic generation method and device based on behavior preferences, equipment and a medium, and the method comprises the steps: obtaining user behavior data, and constructing a user behavior graph structure according to the user behavior data, the complex association comprising similarity between users, association between contacts and interaction weights of the users to the contacts; processing short-term behavior records of the user according to a Transform network, extracting sequence features, storing statistical features of long-term behaviors of the user according to a long-term memory network, and based on a user behavior graph structure, fusing the sequence features and the statistical features to obtain fused features; by constructing the user behavior preference graph and fusing the short-term and long-term behavior characteristics of the user, accurate modeling of the user behavior preference is realized. The graph structure not only captures the global structure of the user behavior, but also can dynamically reflect the current interest of the user; according to the method, the path candidate set is dynamically generated and optimized by adopting reinforcement learning and combining real-time feedback.

The invention relates to the field of drug-target affinity prediction, in particular to a drug-target affinity prediction method based on a multi-scale mixed attention network. According to the method, global residue characteristics can be learned by protein pocket residues by utilizing a self-attention mechanism, so that the global characteristics of the protein are learned. In addition, a cross-modal feature fusion mechanism is utilized, protein and drug structural features are enhanced in sequence features, cross-modal feature fusion in entities is enhanced, meanwhile, a cross-entity interaction module is utilized to recognize key atoms of drug molecules or key residues of protein, and therefore the overall performance of the model is improved. Finally, multi-level protein feature extraction is further introduced to further extract protein features.

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention provides a multi-modal protein function prediction method and system, and the method comprises the following steps: carrying out the processing of a one-dimensional convolution layer and a pooling layer of protein sequence data, extracting the local dependence features in a protein sequence, and capturing the mode and key sequence features in the protein; using protein sequence data as input, and using a large-scale pre-trained protein language model to extract residue-level embedded information in a protein sequence as node features to be applied to the contact graph; the method comprises the following steps: taking protein sequence data as input, predicting the probability of contact between protein residues based on a Cb-Cb distance, and constructing a contact graph; and performing joint processing on features of different modes by using a multi-head attention mechanism and a residual structure, integrating data from different modes through feature fusion, and fusing the extracted key sequence features, the residue level embedded information and the contact graph. The prediction method is accurate in prediction and good in performance.

The invention provides a polar region sea ice thickness inversion method based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the feature extraction of SAR data, MODIS data, AMSR2 data and MIRAS data in a target region, constructing an image and sequence feature data set, carrying out the fusion of the image and sequence feature data set through employing a designed multi-modal data fusion network, and obtaining a polar region sea ice thickness inversion result. A new sequence feature data set is obtained, multiple machine learning regression models are used for training the sequence feature data set, the machine learning regression model with the optimal index is selected for sea ice thickness inversion, optical data are deleted, and a sea ice thickness inversion model is obtained through re-training; and dividing the target area into a clouded area and a cloudless area by using the cloud mask data, and applying corresponding inversion models to the clouded area and the cloudless area respectively. According to the method, the advantages of various remote sensing data in sea ice thickness inversion are fully fused, continuous large-area ice thickness information extraction of sea ice can be realized, and the method has better universality.

The invention provides a pre-training enhanced space-time Transform traffic flow prediction method, which belongs to the field of intelligent traffic, aims to improve the prediction performance in a complex traffic environment by designing a space-time Transform model and a pre-training strategy, and comprises the following steps of: firstly, pre-training an encoder through a mask automatic encoding strategy by utilizing long-term historical traffic flow data, and then pre-training the encoder through a mask automatic encoding strategy; extracting long sequence features by adopting time perception attention embedded with time period information; constructing a pre-training space-time sparse graph generated by a data-driven graph learning layer to capture a dynamic spatial dependency relationship; in the prediction stage, a time feature extraction module and a spatial feature extraction module are designed, an improved multi-head self-attention mechanism is utilized to model correlation between time and space, and long sequence features and spatial-temporal features are adaptively fused through a gating mechanism to obtain a prediction result. According to the method, long-term historical traffic flow sequence information can be effectively extracted, and the traffic flow can be predicted more accurately under a complex traffic condition.

The invention discloses a log anomaly detection method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: extracting feature data from log data, the feature data comprising log sequence features and log semantic features; inputting the log sequence features into a pre-trained strategy network to obtain a prediction result of a log sequence level; inputting the log semantic features into a fine-tuned large language model to obtain a reasoning result of a log semantic level; generating a reward signal based on the prediction result of the log sequence level and the reasoning result of the log semantic level; performing reinforcement learning fine tuning on the strategy network by using the reward signal to obtain an anomaly detection model; and performing anomaly detection on the log data based on the anomaly detection model, and outputting an anomaly detection result.

The invention provides a drug target prediction method based on a 3D structure and a multilevel attention mechanism, and belongs to the technical field of drug target prediction. The technical problem to be solved is to provide a drug target prediction method based on a 3D structure and a multi-level attention mechanism. According to the technical scheme, the method comprises the following steps: collecting data information of an interaction tag among a related drug, a target sequence and a drug target protein, and processing and extracting SMILES character string characteristics of the drug; learning the numeralized protein amino acid sequence by using a convolutional neural network, completing feature coding of a target spot on the sequence through multi-layer convolution and pooling operation, and extracting target spot sequence feature data; a PDB file is read and modeled into a pocket graph, feature calculation is carried out on the pocket graph, a final protein structure representation code is generated, and structural feature data of target protein is extracted by modeling the PDB file; the method is applied to drug target prediction.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention provides a small target detection method and system based on an improved YOLOv8 structure, and the method comprises the following steps: carrying out the preprocessing of a to-be-detected image, so as to obtain an input standardized image; inputting the standardized image into a YOLOv8 backbone network, and extracting feature maps of different scales; the feature map is input to a YOLOv8 neck network for feature fusion, the neck network introduces an ASF mechanism, a scale sequence feature fusion module and a three-feature encoder module are integrated, cross-scale enhancement and integration are performed on features of different resolutions, and a multi-scale fusion feature map is generated; constructing P2, P3, P4 and P5 detection layers based on the fused feature map; the P2-P5 detection layer predicts candidate target frames and category confidence of the candidate target frames respectively; soft non-maximum suppression is adopted to process the candidate target frame, a linear or Gaussian attenuation function is utilized to adjust confidence, a redundant frame is suppressed, and a final detection result is output. According to the method, the recognition capability of small targets in dense and complex scenes is remarkably improved.

The invention relates to the technical field of network security of a power monitoring system, and discloses an attack event prediction method and system based on low-intensity abnormal mode data, and the method comprises the steps: intercepting abnormal data fragments from historical multi-source heterogeneous data based on the low-intensity abnormal mode data selected from the historical multi-source heterogeneous data; analyzing to obtain a target abnormal data fragment; training the gating loop network model based on target key time sequence abnormal features obtained by performing feature extraction on the target abnormal data fragments to obtain a future time sequence feature prediction model; performing structured processing on the low-intensity abnormal event to obtain a spatial correlation feature topological structure; obtaining future time sequence features based on the future time sequence feature prediction model; and obtaining an attack event prediction result based on an abnormal event evolution graph constructed by fusing the future time sequence feature and the spatial correlation feature topological structure. According to the method provided by the invention, accurate identification can be carried out in the preparation stage of the attack event.

The invention discloses a text abstract generation method and system based on sparse attention acceleration, and the method comprises the steps: reading long text data, carrying out word segmentation and embedded coding processing, extracting a sequence feature vector, and mapping the sequence feature vector into a query matrix Q, a key matrix K and a value matrix V; constructing an abstract generation network, wherein the abstract generation network comprises a sparse attention calculation module, a feedforward calculation module, a prediction head module and a key value cache module; inputting the query matrix Q, the key matrix K and the value matrix V into an abstract generation network, passing through a backbone network formed by stacking a sparse attention calculation module and a feedforward calculation module for multiple times, processing by a prediction header module, and caching a historical decoding state in real time through a key value caching module to obtain an initial text feature vector; and based on the initial text feature vector, executing an autoregressive decoding process through an abstract generation network, and outputting a final abstract result. According to the method, the decoding process can be accelerated, and the semantic integrity and coherence of the generated abstract are ensured.

The invention discloses an antibacterial peptide recognition method and system based on a sequence-structure dual-channel neural network, and the method comprises the steps: splicing amino acid features and amino acid-level manual features extracted by ProtT5 to obtain peptide embedding, and transmitting the peptide embedding to a sequence channel composed of a plurality of Transform blocks to extract the sequence features of the peptide; predicting a three-dimensional structure of the peptide by using ESM-Fold to construct an adjacency graph, taking amino acid features obtained by ESM-2 as node features of the adjacency graph, and performing layer-by-layer extraction and enhancement by fusing structural channels of multi-head graph attention, a residual network, layer normalization and a feedforward neural network; and carrying out maximum pooling and splicing on the sequence features and the structural features, and then, carrying out antibacterial peptide prediction. According to the method, a multi-feature fusion strategy is adopted, meanwhile, the three-dimensional structure information of the antibacterial peptide is introduced, and the sequence and the structural features are fused through a two-channel architecture, so that the recognition accuracy of the antibacterial peptide is effectively improved.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention provides a distribution network traveling wave fault point intelligent positioning method and system based on deep time sequence feature learning, and belongs to the technical field of intelligent power distribution detection based on deep learning. Firstly, a high-speed traveling wave sensor is arranged on a distribution line, multi-dimensional three-phase voltage and current signals are collected, and a data set of fault types, phases, grades and positions is constructed; then, fault state features are extracted through topology perception normalization, symmetric component mapping and two-channel time sequence modeling, and accurate recognition of fault types, related phases and grades is achieved through an attention mechanism; furthermore, a fault intelligent positioning model based on a topological graph is constructed, a tower sensing weight and a line information bearing weight are introduced, and space-time embedding is extracted through a graph attention network and an echo state network, so that line fault classification and accurate positioning are realized. Finally, through combination of off-line model training and on-line system deployment, real-time identification and positioning of power distribution network faults are realized, and accuracy, robustness and response speed of fault diagnosis are effectively improved.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention discloses a mine slope risk prediction method based on multi-modal deep learning, and relates to the technical field of mine slope risk prediction, and the method comprises the steps: Stepl: inputting multi-modal data, Step2: preprocessing effectiveness data, Step3: constructing a sample set, and Step4: realizing mine slope stability prediction based on a gating cycle unit GRU of a mutual information mechanism. According to the invention, a multi-modal data integration GRU network is adopted to carry out sequence modeling on fused data, more complex sequence features are extracted, slope safety event prediction analysis is completed, high-precision monitoring of a mine slope is realized, and the accuracy and comprehensiveness of a prediction result are improved; according to the method, the mutual information mechanism is applied to the GRU network to maximize the mutual information between the sample and the feature, the dependency between the feature and the label is improved, the prediction capability is guaranteed, and the generalization capability on new data is also guaranteed.

The embodiment of the invention provides a water quality index prediction model training method and device, equipment and a storage medium, and the method comprises the steps: carrying out the data enhancement of a training data set through employing a meta-learner network after the training data set is obtained, so as to obtain an enhanced time series data set. And inputting the enhanced time sequence data set into a trained model, and calculating training loss through the trained model, thereby optimizing model parameters of the trained model according to the training loss. According to the method, data enhancement can be carried out by utilizing a meta-learner network, layered contrast learning is carried out on multiple dimensions of global, instance and time, and information of a time sequence on different semantic hierarchies is comprehensively captured. The sequence features are extracted by using the convolutional neural network, and the self-supervised hierarchical comparison model is trained and established, so that the accuracy of the water quality index prediction result can be improved, and the stability is relatively high.

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention relates to the technical field of DNA fragment data processing, in particular to a DNA fragmentation gene detection data processing method based on artificial intelligence. Segmenting the DNA fragmentation detection data into a plurality of local data blocks, and executing continuous homology analysis on each local data block; dynamic weights are distributed to annular and branch structures in the multi-scale topological feature matrix by using an attention mechanism; extracting a base correlation mode of a base sequence in the DNA fragmentation detection data through one-dimensional convolution to obtain sequence features; performing tensor splicing on the weighted topological features and the sequence features in a hidden space; performing coarse-grained classification on the fused high-order feature vectors through a multi-layer perceptron, and outputting a preliminary classification result; and constructing a lightweight neural network sub-module based on the preliminary classification result to obtain a final classification result, thereby effectively improving the accuracy and interpretability of the classification result.

The invention discloses a multivariate feature-based liquid-liquid phase separation protein prediction method, which comprises the following steps of: extracting sequence features, secondary structure features and spatial structure features during prediction, and constructing a residue contact diagram according to a spatial structure; a graph attention network is adopted to extract a spatial structure graph constructed according to a residue contact graph and sequence embedding to excavate deep structure features, and prediction performance is effectively improved based on the characteristics closely related to protein phase separation behaviors and structures thereof; besides, according to the scheme, a three-layer stacked graph attention network is adopted to extract structural features of the protein, physical and chemical features extracted from the sequence are combined, the liquid-liquid phase separation protein is predicted through a stacked integrated learning model, and the model prediction performance can be further improved.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

The invention discloses a multi-modal data fused shield tunneling machine cutter state fault prediction system, which comprises a state acquisition module for acquiring multi-modal state data, decomposing the multi-modal state data into time and space related characteristics, and obtaining optimal state data through low-rank matrix decomposition and compression; the anomaly simulation module is used for collecting fault state data, constructing an improved lightweight weight migration network to perform cross-domain feature extraction to obtain a cross-domain common feature vector, and obtaining common features through a feature alignment algorithm based on adversarial training on the basis of the cross-domain common feature vector and the fault state data; and the fault prediction module is used for performing fault prediction through a space-time cross attention dynamic migration network on the basis of the obtained optimal state data and common characteristics, so that long sequence characteristics can be captured when the cutter is abnormal, the calculation amount can be reduced when the cutter is normal, and the real-time performance and the accuracy of fault prediction are improved.

The invention relates to a method for classifying antihypertensive peptides by fusing sequence and structure multi-modal features and combining contrast-generative combined optimization. The method comprises the following steps: extracting sequence feature representation and structure feature representation of peptide fragments; performing multi-modal feature enhancement of comparison-generative joint optimization on the sequence feature representation and the structural feature representation; and performing peptide classification on the enhanced feature representation by using a preset Kan-Conv structure and tag smooth joint optimization classification model. According to the method, high-precision recognition of the functional activity of the antihypertensive peptide is achieved, information in the three aspects of the sequence, the structure and the generative potential space is creatively and comprehensively utilized, the blank that the structure-sequence synergistic effect is ignored in the existing peptide function prediction field is filled, and the screening efficiency and prediction reliability of the antihypertensive peptide are remarkably improved.