137 results about "Hypersphere" patented technology

The invention discloses a communication interference signal open set identification method, system and device and a medium. The method comprises the following steps: carrying out short-time Fourier transform on a communication interference signal to obtain a time-frequency graph; performing multi-scale feature representation on the time-frequency diagram by using a Res2Net-based multi-scale feature extraction network; constructing a projection layer to carry out hyperspherical embedding on the multi-scale features, carrying out modeling by utilizing vMF distribution, and designing a compactness and dispersity loss function; multi-scale features after modeling are input into an OpenMax layer for open set recognition, output of a projection layer is used as an activation vector to improve OpenMax, Euclidean distance is replaced with cosine distance, input sample unknown is calculated based on an extreme value theory, category probability distribution is adjusted, and some abnormal samples can be classified into unknown categories; the Res2Net more flexibly captures multi-scale information than the convolutional network; the separation of inter-class features and the compactness of intra-class features are effectively enhanced through the hyperspherical vMF distribution; the OpenMax layer endows the network with unknown category identification capability; the method has better performance and wider universality; the system, the equipment and the medium are used for realizing the communication interference signal open set identification method.

The invention discloses a hyperspectral image open set classification method based on prototype similarity distribution modeling. The method comprises the following steps: acquiring a training sample set and a test sample set; initializing a category prototype on the unit hyper-sphere and forming uniformly distributed prototype spaces through iterative optimization; training a deep learning network based on a joint loss function, guiding the samples to form high-discrimination similarity distribution in a prototype space, and calculating similarity vectors of the samples and prototypes of each category; training the OCSVM by using the similarity vector of the known class sample to learn the distribution boundary of the known class in the prototype space; in the test stage, a test sample is input into the deep learning network, the similarity vector of the test sample is calculated, and whether the test sample belongs to a known class or not is judged by the OCSVM; and if the class is a known class, determining the fine-grained classification according to the class prototype of the maximum similarity component in the similarity vector. According to the method, the interpretability and generalization of the open set classification model are enhanced, and the misrecognition risk between similar categories is reduced.

The invention discloses a universal domain adaptive image classification method based on distance entropy weighting, and the method specifically comprises the steps: 1, obtaining source domain data and target domain data, 2, building a model, and carrying out the pre-training; step 3, mapping the source domain data and the target domain data to a hypersphere; 4, setting all prototypes of the source domain as 0; step 5, calculating a source domain category weight # imgabs0 # and a target domain sample weight # imgabs1 #; step 6, performing adversarial training by using a weight weighting training model and a domain discriminator D, and determining whether the data is from a source domain or a target domain; obtaining weighted confrontation loss; and 7, screening a target domain sample to obtain a target domain auxiliary domain, and outputting an image for classification. According to the universal domain adaptive image classification method based on distance entropy weighting disclosed by the invention, the problem that image classification is not clear due to the fact that a model in the prior art cannot effectively distinguish and recognize private categories of a target domain is solved.

The invention provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.

The invention discloses a single-classification industrial control system anomaly detection method based on double-view information bottleneck fusion, which belongs to the technical field of industrial control system anomaly detection and comprises the following steps of: in a training stage, simultaneously constructing a time sequence view and a pseudo image view; respectively extracting characterization through a time dynamic encoder and a pre-trained visual backbone; performing cross-view compression in the variational information bottleneck fusion module and establishing projection branches only depending on sequence representation; a normal subspace is learned around a center vector, an accurate hypersphere is established to describe normal data, and a center distance is used as an abnormal score and a quantile threshold is used to complete discrimination; in the detection stage, online recognition can be performed only by inputting a time sequence view. According to the method, dual-view prior and variational information bottleneck are fused, end-to-end collaborative optimization is carried out on the dual-view prior and the variational information bottleneck and deep support vector data description, an accurate decision boundary can be established under the condition of a small number of samples, and a solution for industrial control system anomaly detection under the condition that the samples are limited is provided.

The invention provides an incomplete vibration data damage identification method and system fusing comparative learning and self-supervised learning, and aims to solve the problems of data scarcity and unbalanced and incomplete data sets in civil engineering structure damage identification, structure acceleration data is acquired through a vibration sensor, data enhancement is performed after preprocessing, and the structural acceleration data is acquired through a vibration sensor. Comprising random mask enhancement and white noise enhancement, a neural network model based on an auto-encoder is constructed, and the neural network model comprises an encoder, a reconstructor, a classifier, a predictor and a dimension rising device. Low-dimensional features are mined through self-supervised learning, feature distribution is optimized in combination with comparative learning, the similarity of similar features and the distinction degree of different types of features are maximized, and finally the health state, known damage, unknown damage and fuzzy damage states of the structure are judged through the hyper-sphere boundary of a high-dimensional decision space. According to the method, the accuracy and robustness of damage identification are remarkably improved, and particularly, the method has excellent performance in the aspect of slight damage identification.

The invention belongs to the technical field of multi-view clustering, and discloses an end-to-end multi-view clustering method and system for dynamic cross-view node interaction. The method comprises the following steps: constructing graph structure information according to node features in a multi-view data set; and inputting the graph structure information and the node features into the multi-view graph neural network model to obtain a clustering result. The multi-view graph neural network model comprises an encoder, a multi-layer perceptron, a decoder and a hyperspherical clustering module with regularization constraint; the encoder comprises a plurality of encoder layers, and each encoder layer comprises an in-view representation learning module and a cross-view representation learning module. According to the method, an unsupervised end-to-end node-level cross-view message transmission mode is provided, direct information interaction between cross-view nodes is established, complementary information propagation and consistent semantic information learning are effectively promoted, clustering is carried out by adopting a hypersphere with regularization constraint, and the clustering efficiency is improved. Balanced distribution and good separability between different categories can be ensured.

The invention relates to the technical field of fault positioning, and discloses a fault position positioning method and device based on multiple acquisition devices, equipment and a medium, and the method comprises the steps: obtaining a plurality of device positions of the acquisition devices of a plurality of traveling wave signals, the time difference of arrival of the traveling wave signals acquired by every two acquisition devices, and the propagation speed of the traveling wave signals; a plurality of hypersphere equations are established according to the positions of every two devices, the time difference of arrival and the propagation speed, and the hypersphere equations are used for reflecting the difference value relation of the distances between the positions of every two devices and the positions of the fault points corresponding to the traveling wave signals; and using a preset particle swarm optimization algorithm and a hyperspherical equation to carry out fault point position positioning processing to obtain an optimal fault point position. Through the above mode, a plurality of acquisition devices can be adopted to capture traveling wave signals, then the hyperspherical equation is established, and finally, the particle swarm optimization algorithm and the hyperspherical equation are used to carry out fault point position positioning processing, so that the optimal fault point position is obtained, and the fault positioning precision is improved.

The invention discloses a rapid yield analysis method based on xgboost proxy model truncation sampling, and the method comprises the following steps: generating a first random sample point set, and obtaining a training set through simulation; constructing a proxy simulator, and training the proxy simulator by using the training set; generating a second random sample point set, and inputting the second random sample point set into the proxy simulator to obtain a pseudo data set; establishing a filter, determining two hyperspherical surfaces based on the pseudo data set, dividing a process parameter space, evaluating a safety coefficient and the probability that a sample point enters a real simulator, and simulating the sample point of the process parameter space according to the probability to obtain a real sample set; iteratively optimizing the hyper-spherical surface and the proxy simulator by using the real sample set until convergence to obtain a final safety coefficient; and generating a third random sample point set, carrying out truncation sampling and failure discrimination by combining a real simulator and an agent simulator, and carrying out statistics on the final yield. According to the method, the xgboost network is adopted as an agent model to reduce the simulation cost, the sample size is reduced through truncation sampling, and rapid and accurate prediction of the yield of the integrated circuit is realized.

The invention discloses an SVDD-based operation condition classification method and system for autonomous shovel loading of a loader, and the method comprises the steps: collecting the autonomous operation data of the loader under different operation conditions, and carrying out the preprocessing of the autonomous operation data, so as to extract a shovel loading operation segment and the characteristics of the shovel loading operation segment; training an SVDD model under each working condition by using the features, obtaining the center and radius of a hypersphere of each working condition grade category, and constructing an SVDD category incremental learning model; when new operation data are input, the model can judge whether the data belong to known working conditions or not; if yes, calculating a support function value to determine a specific working condition grade; and if not, identifying the working condition as a new working condition and updating model parameters. According to the method, by collecting and analyzing the operation data of the loading machine and utilizing the SVDD model and the incremental learning ability of the SVDD model, precise classification and dynamic adaptation of the operation working conditions under all working conditions are achieved, and the autonomous operation efficiency and precision are improved.

The invention discloses a memory enhancement action recognition method and system on a Riemannian manifold and a storage medium, and the method comprises the steps: 1, collecting human body action data, and representing the human body action data as a third-order tensor; 2, expanding along three modes to obtain three corresponding matrixes; 3, calculating by adopting a human short-term memory mechanism to obtain a memory enhanced weight matrix; 4, decomposing the weight matrix through a principal component analysis method to obtain a base vector with a weight; 5, recombining, normalizing and mapping to a unit hyper-sphere, and reserving an angle relation; 6, learning modal weight parameters through a Monte Carlo Markov algorithm, and calculating geometric differences between points on the hypersphere; and 7, carrying out human body action classification by adopting a K-nearest neighbor classifier. The method effectively solves the problem of time information loss in a complex scene, and is suitable for various application scenes such as medical health, virtual reality, physical training and the like.

The invention relates to the technical field of electric digital data processing, in particular to a weak supervision event representation learning method and system for aid decision making, and the method comprises the following steps: forming an event sequence through the operation behaviors of a user on an operation and maintenance network, and generating an initial event representation; constructing a normal prototype of the multi-hypersphere; performing first-stage training to obtain a sequence encoder after first-stage parameter optimization and a normal prototype of the multi-hyper-sphere; calculating an abnormal score of each event in the event sequence to obtain a prediction result of the event; carrying out multi-instance learning, and carrying out second-stage training to obtain a sequence encoder after second-stage parameter optimization, a normal prototype of the multi-hypersphere and an anomaly discriminator; abnormal events are screened, and manual decision making of the intelligent operation and maintenance system is assisted. The method and the system provided by the invention provide better assistance for decision making of the intelligent operation and maintenance system.

The invention belongs to the technical field of microservice system anomaly detection, and discloses a Trace anomaly detection method under a microservice architecture based on a graph attention network. A service operation graph (SOG) is constructed to serve as a mesoscopic layer trace representation method, and the graph representation granularity and efficiency of an existing method are balanced. Meanwhile, a group of delay-related features and error propagation modes are extracted as multi-dimensional edge features to be integrated into the SOG, and interaction information between services is fully utilized to improve the accuracy of anomaly detection. In order to capture various index data generated in the service and a calling relation between the services, the characteristics of nodes and edges of SOG are learned through GAT, and a graph embedding vector of a trace is obtained. And a hypersphere loss function training model is utilized to avoid dependence on labels, normal traces are gathered to a hypersphere center, and traces far away from the hypersphere center are regarded as anomalies.

The invention discloses an LSTM-SVDD anomaly detection method and system based on rule guidance, belongs to the technical field of industrial nondestructive quality inspection, and aims to solve the technical problems that noise samples are sensitive and excessively depend on annotation of abnormal data, and expert experience and knowledge cannot be effectively introduced. Constructing a feature extraction model based on a bidirectional LSTM network, a rule template engine and a feature projection fusion layer; calculating a hypersphere radius through a quantile method, constructing an SVDD loss function as a basic loss function, introducing a rule penalty term to construct a rule loss function, carrying out weighted summation on the basic loss function and the rule loss function to construct a total loss function, and obtaining a trained feature extraction model and an optimized hypersphere radius through minimizing the total loss function; and inputting a to-be-detected sample into the trained feature extraction model, calculating an Euclidean distance between a fusion feature vector and the center of the hypersphere, and comparing the Euclidean distance with a dynamic detection threshold value.

The invention discloses a unified unsupervised depth forgery detection method based on prototype guidance and double hyperspheres, and the method comprises the following steps: S1, extracting visual artifact features generated by depth forgery, and achieving the generation of pseudo labels through the clustering of a Gaussian mixture model; s2, performing comparative learning through a category prototype of momentum updating; and S3, realizing effective fusion of a feature space and a geometric decision by respectively constructing independent hyper-spheres for real and forged samples, and constructing a dual-depth support vector data description framework. The system has the beneficial effects that the system is composed of three core modules: a visual artifact feature-based pseudo label generator provides a reliable supervision signal, and the visual artifact feature-based pseudo label generator provides a visual artifact feature-based pseudo label description framework; a prototype guided contrast learning (PGCL) module enhances the discrimination capability through a prototype of momentum update, and a dual-depth support vector data description (Dual-DeepSVDD) module constructs a dual-hyperspherical decision boundary of true and false samples, thereby realizing effective integration of feature learning and geometric decision.

The invention relates to a micro-expression recognition method for cross-domain feature center assisted emotional intensity invariance feature extraction, belongs to the technical field of deep learning and pattern recognition, designs a cross-domain feature center assisted emotional intensity invariance feature extraction network, and fully utilizes an existing macro-expression data set to assist micro-expression recognition. Macro-expression related features are utilized to guide learning of micro-expression features, a neural network is helped to learn more emotion related features, hyper-spherical constraint is carried out on the micro-expression features and the macro-expression features, multiple angle optimization strategies are designed, intensity information of the features is weakened, angle information is focused, and the effect of improving the accuracy of the micro-expression features is achieved. In addition, a macro-expression feature center and a micro-expression feature center are designed to cooperatively guide training of the network, and a model is helped to learn more compact emotional feature distribution.

The invention discloses a robot virtual-real cooperative training decision optimization system and method based on digital twinning, and the method comprises the following steps: collecting state data and disturbance data of an entity robot in a real environment, and carrying out the preprocessing of the data to generate standardized input; joint coding and state perturbation mapping are carried out on the standardized data, and a feature vector sequence embedded in a hyperspherical manifold space is generated; inputting to a virtual twin control body based on a hypersurface neural element structure, executing disturbance direction sensitive activation, and outputting an activated state vector sequence; virtual and entity control action prediction sequences are generated respectively, an embedded space difference vector is calculated, and control body parameters are updated based on a consistency optimization criterion; after convergence, the control body executes reasoning to generate a target control action sequence, the entity robot is driven to complete action execution, and control strategy optimization is achieved. According to the method, high-precision migration and rapid convergence of a robot control strategy are realized, and the execution stability in a complex disturbance environment is improved.

The invention discloses a method and a system for monitoring pressure abnormity of a fuel oil common rail pipe of a marine main engine. The method comprises the following steps: acquiring historical operation parameter data and constructing a data set; the historical operation parameter data comprises historical host load data, historical host rotating speed data and historical fuel oil common rail pressure data; historical operation parameter data are preprocessed, an SVDD model is constructed and trained based on the historical operation parameter data, the model obtains a historical normal data hyper-sphere in a three-dimensional feature space, and the center and the radius of the hyper-sphere are obtained; similarly, real-time operation parameter data collected in real time are preprocessed and then input into the model, the distance between the real-time operation parameter data and the center of the hypersphere is obtained, and the value of the distance is compared with the value of the radius of the hypersphere in historical normal data; and when the numerical value of the distance is large, it is judged that the real-time operation parameter data is within an abnormal range, the fuel oil common rail pressure of the ship main engine is in an abnormal state, and an alarm signal is triggered. The method has the effect of low error rate.

The invention discloses a hyperspectral image open set classification method and device based on fractional domain information enhancement and a hypersphere prototype learning strategy, and belongs to the technical field of hyperspectral image open set classification. In order to solve the problem that a high misclassification risk exists between a known category and an unknown category in an existing hyperspectral image classification method, the method comprises the following steps: firstly, obtaining fractional domain information of hyperspectral data based on weighted fractional Fourier transform, and then fusing the fractional domain information with spatial spectral domain information; deep feature extraction is carried out on the hyperspectral image through a double-branch network, a hypersphere prototype learning strategy is adopted, utilization of a measurement space is optimized, and features of known categories are restrained to be evenly distributed on a hypersphere; and carrying out identification based on a closed set classifier of a known category prototype, and meanwhile, realizing open set identification by utilizing a hypersphere prototype radius so as to obtain a final open set classification result.

The present application relates to a kind of single class image recognition method based on multiple hypersphere space division, belong to image recognition and machine learning technical field.First, sample is mapped to a latent space by feature extraction network, then according to the similarity of training sample and test sample in space, and the classification identification of test image sample is carried out.The way of introducing threshold radius is used to judge whether the distance of test sample from its nearest neighbor training sample is close enough.The representation of target category in feature space is the space formed by the superposition of multiple hypersphere regions, with threshold as the radius of each hypersphere, if the feature of a test sample is within the space range formed by the multiple hyperspheres, it will be predicted as positive sample, otherwise it will be predicted as negative sample.The prediction accuracy of the present application for test image sample is significantly improved, and the prediction of positive sample and negative sample in test image sample is more balanced.

The application discloses an iris recognition method and device, system and storage medium, and performs preprocessing such as polar coordinate unfolding on an input original iris image in sequence; multi-level image features are extracted through an inverse residual module; subsequently, a global depth convolution GDConv operator is introduced to replace a global average pooling layer, so that spatial structure distribution features of iris textures are retained under the premise of extremely low calculation complexity; finally, an ArcFace loss function is used to optimize the distribution of features in a hyperspherical space, and the class separability of the features is enhanced by introducing an additive angle interval. By using the technical scheme of the application, the technical problems that spatial topological information is lost due to a global average pooling GAP operation in iris feature extraction by using an existing lightweight convolutional neural network, and the feature discrimination is reduced due to network lightweight are solved.

This invention discloses a continuous vector discretization representation method, system, and application, comprising: constructing an end-to-end unified architecture including an attention encoder, a binary spherical quantization module, and an attention decoder; utilizing the attention encoder combined with a block causal masking mechanism to uniformly extract high-dimensional feature vectors from single-frame images or multi-frame videos; performing dimensionality reduction, spherical normalization, and binary quantization based on a learnable hyperplane on the high-dimensional feature vectors through the BSQ module, mapping the features to a unit hypersphere and generating binary discrete codes without an explicit codebook; and using the attention decoder combined with spatiotemporal position coding to perform high-fidelity reconstruction of the discrete codes. This invention solves the problems of low parameter efficiency, incompatibility with image and video processing, imbalance between reconstruction quality and computational efficiency, and unstable training caused by the reliance on explicit codebooks in existing technologies. It achieves lightweight, high-fidelity, wide compatibility, and easy training, making it particularly suitable for efficient storage, transmission, and accurate reconstruction of multimodal medical images.

The invention discloses an intelligent monitoring, prevention and control method and system for the operation condition of an escalator, and the method comprises the steps: determining optimal installation parameters, installing a high-definition camera in an operation region of the escalator, obtaining an image of the operation condition of the escalator, screening scanning data and operation data through a hypersphere, and obtaining abnormal data; determining an early warning result according to abnormal data and a danger threshold value, correcting the operation condition image to obtain an operation condition correction image, inputting the operation condition correction image into a pre-trained escalator danger identification model to obtain an early warning result, and transmitting the early warning result, the abnormal data and the operation condition image to a control center for storage, and the control center executes a prevention and control strategy according to the early warning result. The method not only can improve the running safety and reliability of the escalator and effectively reduce the fault occurrence rate and the accident risk, but also has good interpretability, and can be directly applied to an intelligent monitoring, prevention and control system for the running condition of the escalator.

This application belongs to the interdisciplinary field of artificial intelligence, computer vision, and network information security. It discloses a deepfake detection method and system based on orthogonal subspace decomposition and hyperspherical metric. By applying singular value decomposition to the weight matrix of a pre-trained visual model, it explicitly constructs a frozen principal subspace that preserves general semantic knowledge and a trainable orthogonal residual subspace that captures specific forgery traces, achieving orthogonal isolation of the parameter space. Simultaneously, hyperspherical metric learning is introduced into the feature space, performing L2 normalization on the features and applying alignment and uniformity losses. Combined with spherical linear interpolation, latent space data augmentation is performed while preserving the Riemannian geometric structure. Through the synergistic constraints of the parameter and feature spaces, this application can reduce the interference of fine-tuning on pre-trained general visual knowledge and improve the feature discrimination stability and cross-forgery generalization ability in deepfake detection tasks.

The invention provides an industrial anomaly detection method based on an improved coupling hypersphere feature adaptive model. The method comprises the following steps: acquiring an original industrial image data set; constructing an improved coupling hypersphere feature adaptive model, wherein the improved coupling hypersphere feature adaptive model combines partial convolution and dynamic upsampling to enhance the multi-scale feature fusion capability; taking a training set as input, performing iterative alignment increment memory bank optimization on a memory bank in the improved coupling hypersphere feature adaptive model, and constructing normal feature representation; taking the training set as input, and training the improved coupling hypersphere feature adaptive model based on the constructed normal feature representation; and taking the test set as input, and carrying out anomaly detection and positioning by adopting the trained improved coupling hypersphere feature adaptive model. According to the method, the accuracy of unsupervised industrial anomaly detection and the pixel-level positioning precision are improved by optimizing the feature up-sampling precision and the memory bank characterization capability.

The invention belongs to the technical field of aero-engine gas path fault diagnosis, and discloses a gas path fault hyper-spherical clustering method based on angle interval constraint, comprising the following steps: S1, acquiring a gas path fault data sample and a fault-free data sample; s2, extracting features in the gas circuit fault data sample and the fault-free data sample, and obtaining sample features; s3, calculating a characteristic residual error according to the sample characteristics, and calculating a residual error difference according to the characteristic residual error; s4, constructing an angle interval constraint loss function, an L2 norm constraint loss function and a class center aggregation function; s5, constructing a total loss function; and S6, inputting the sample features, the feature residuals and the difference of the residuals into a total loss function to obtain a hyperspherical clustering result. The accuracy and robustness of gas path fault diagnosis are improved, and assistance is provided for health management and gas path fault analysis of an engine.

The present disclosure relates to a user financial risk identification model generation method and device, electronic equipment and computer readable medium. The method comprises: obtaining financial data of a plurality of historical users, wherein the financial data comprises a default state; selecting a plurality of positive sample users from the plurality of historical users according to the default state; generating a hypersphere equation set through the financial data of the plurality of positive sample users and an initial hypersphere equation; solving the hypersphere equation to obtain an optimal solution; and generating a user financial risk identification model based on the optimal solution, wherein the user financial risk identification model is used to determine the financial default risk probability of a user. The user financial risk identification model generation method and device, electronic equipment and computer readable medium disclosed by the present disclosure can adapt to the machine learning model training process when the number of samples is unbalanced, and can also generate an accurate and efficient user financial risk identification model using unbalanced sample data.

The invention discloses a power distribution network power supply self-healing method based on a hypersphere clustering algorithm, and the method comprises the steps: constructing a fault recovery database, and carrying out the secondary kernel cross-integral hypersphere clustering of fault data in the fault recovery database, so as to construct a hypersphere clustering model; determining a clustering maximum range constraint condition of the hyper-sphere clustering model, and determining a cross constraint condition of the hyper-sphere clustering model; when the power distribution network has a fault, matching the fault data of the current fault with the fault data of the historical fault according to the hypersphere clustering model, and if matching succeeds, executing a corresponding power supply recovery scheme in the fault recovery database; and if the matching is unsuccessful, constructing a new power supply recovery scheme, recovering power supply for part of lines or all lines according to the new power supply recovery scheme, adding fault data corresponding to the fault to the hypersphere clustering model, and updating the hypersphere clustering model. According to the design, the speed of calling the fault recovery scheme can be increased, and the power supply self-healing efficiency is improved.

The present application discloses a text classification method, apparatus, storage medium, and device. The method comprises: first obtaining a target text to be classified, inputting the target text into a pre-built text classification model, and identifying a hyperplane feature vector corresponding to the target text; then respectively calculating the Euclidean distances between the hyperplane feature vector corresponding to the target text and the hyperplane feature vectors corresponding to the center points of N preset categories; wherein N is a positive integer greater than 0; then taking the preset category corresponding to the minimum Euclidean distance among the obtained N Euclidean distances as the target category, and determining the relationship between the minimum Euclidean distance and the radius of the hypersphere where the target category is located to obtain a judgment result, and then classifying the target text according to the judgment result to obtain a classification result of the target text. Thus, when performing text classification, human intervention is no longer required, thereby improving the accuracy of the text classification result.