In order to improve the accuracy and efficiency of Internet of things illegal intrusion identification, an automatic identification method of Internet of things illegal intrusion based on unsupervised learning is proposed. Firstly, in order to accurately and automatically identify the illegal intrusion of the Internet of things, the gradient descent clustering method is used to fully collect the illegal intrusion data of the Internet of things. Secondly, the kernel principal component analysis method is used to extract the characteristics of online intrusion data. Finally, unsupervised learning neural network is used to train the above extracted intrusion data features to complete the automatic identification of illegal intrusion in the Internet of things. The experimental results show that compared with the traditional intrusion recognition methods, the recognition accuracy and efficiency of this method have been significantly improved.