Network big data long text multi-label classification method and system, equipment and medium

Abstract

The embodiment of the invention provides a network big data long text multi-label classification method and system, equipment and a medium and belongs to the technical field of data processing. The method comprises steps of acquiring an original data set; analyzing the original data set to construct a keyword table corresponding to each label, and performing preprocessing to obtain text data; converting the text data into word vectors and word vectors, and calculating position vectors corresponding to different words in the text data; obtaining an embedded vector; inputting the embedded vector into a void gate convolutional layer for coding to obtain a coding vector; and extracting features of the coding vectors according to the self-attention mechanism model to obtain relevance of each word in the text data, and forming a classification result. According to the scheme, the multi-label long text is segmented by constructing the keyword table, the text data is converted into different vectors and then coded, then the self-attention mechanism is used for extracting the features to obtain the relevance of each word, the classification result is formed, and the classification efficiency and accuracy are improved.

Description

technical field [0001] The embodiments of the present disclosure relate to the technical field of data processing, and in particular to a method, system, device and medium for classifying network big data with long text and multiple labels. Background technique [0002] At present, the advent of the Internet era has brought a series of new challenges to big data governance. However, due to the wide range of data in network big data, the processing methods for different types of network big data are not the same, so the network big data Fast splitting and classification processing are crucial for the next step of big data analysis and further processing. At present, methods based on traditional neural networks have good results in common network big data multi-label classification. Among them, LSTM is suitable for short-text network big data, and TextCNN is suitable for long-text network big data. However, when faced with document-level network big data, the effect of common ...

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Problems solved by technology

[0007] In view of this, the embodiments of the present disclosure provide a network big data long text multi-label classification method, system, device and medium, which at least partially solve the problems of poor classification efficiency and classification accuracy in the prior art

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