The invention relates to a scientific and technological literature big data classification method, and belongs to the technical field of big data text mining. The method comprises the steps that S1, atopological relation graph is constructed, wherein the graph is composed of nodes and edges, and the nodes are keywords in literatures, sentences and STKOS; wherein the edges are literatures and sentences, literatures and keywords, sentences and sentences, sentences and keywords and relationships between keywords; S2, converting the topological relation graph into a topological relation matrix; S3, training the classification model by using the training data and the topological relation matrix constructed by the training data; and S4, literature classification: inputting the batch of literatures to be classified into the trained classification model to obtain the probability that the literatures to be classified belong to different categories. Compared with the prior art, the topologicalrelation graph and sentences constructed by the method consider the factors of the word order, and the keywords are terms indexed by experts, so that the classification accuracy is improved; and the adopted classification model does not need repeated training, and sampling calculation is carried out on the input of each convolution layer, so that the classification efficiency is improved.
Scientific and technological literature big data classification method