In order to solve the problem that traditional intrusion detection methods can not effectively solve instrusion data in high-dimensional networks, an intrusion detection method based on Stacked Sparse Denosing Autoencoder (SSDA) network was proposed. Firstly, SSDA was used to perform dimensionality reduction on the intrusion data. Then, the highly abstracted low-dimensional data was used as input data of softmax classifier to realize intrusion detection. Finally, in order to improve original intrusion data decoding ability of the network and intrusion detection ability of the model, an Improved model based on SSDA (ISSDA) was proposed, with new constraints added to the autoencoder. The experimental results show that compared with SSDA, ISSAD's detection accuracy of four types of attacks was improved by about 5%, and the false positive rate of ISSAD was also effectively reduced.