In the face of complex network attacks, in order to ensure network security, an automatic identification method of computer network illegal intrusion based on big data feature mining is proposed. In this method, K-means clustering algorithm is used to cluster computer network data to obtain abnormal data of computer network and reconstruct the phase space of abnormal data. It is used as the input of the least squares support vector machine to establish an automatic identification classifier for illegal intrusion. By constructing a classification plane to automatically identify the types of illegal intrusion in computer networks, the purpose of accurately and automatically identifying illegal intrusion in computer networks is realized. The experimental results show that this method has a good clustering effect on abnormal data of computer network, and can accurately and automatically identify illegal intrusion of computer network. By fully considering the complexity and computational efficiency of the algorithm, redundant calculation and unnecessary memory access are reduced, and this method is feasible to automatically identify illegal intrusion of computer network.