To quickly and accurately identify network attacks in a multi-dimensional environment with diversified attack forms and massive intrusion data， an intrusion detection model combining Fisher-PCA feature extraction and deep learning is proposed. Firstly， the Fisher feature selection algorithm selects important features to form feature subsets. Then the dimension of the feature subsets is reduced based on principal component analysis （PCA） and the feature set with strong classification ability is extracted. A new deep neural network （DNN） is constructed to identify and classify network attack data and normal data. Experimental results on KDD99 dataset show that compared with the traditional artificial neural network（ANN） and support vector machine（SVM） algorithms， the accuracy of this intrusion detection algorithm can be improved by 12.63% and 6.77%， respectively， and the false alarm rate is reduced from 2.31% and 1.96% to 0.28%. Compared with DBN4 and PCA-CNN algorithms， its accuracy and detection rate are basically the same， while the false alarm rate is lower.