Disclosed is a TextCNN-based knowledge extraction method, comprising: S10, constructing a word vector dictionary and a phrase vector dictionary; S20, constructing a first convolutional neural network, and training the first convolutional neural network on the basis of a first optimization algorithm, wherein the first convolutional neural network comprises a first embedded layer, a first multilayer convolution and a first softmax function successively connected to each other; S30, constructing a second convolutional neural network, and training the second convolutional neural network on the basis of a second optimization algorithm, wherein the second convolutional neural network comprises a second embedded layer, a second multilayer convolution, a pooling layer, two fully connected layers and a second softmax function successively connected to each other; and S40, extracting, according to an entity annotation prediction output by the trained first convolutional neural network and an entity relationship prediction output by the trained second convolutional neural network, a knowledge graph triple of data to be predicted. The convolutional neural network has a parallel computation characteristic, and therefore, computational resources can be fully utilized to realize improvement in the efficiency of computation.
TEXTCNN-BASED KNOWLEDGE EXTRACTION METHOD AND APPARATUS, AND COMPUTER DEVICE AND STORAGE MEDIUM