The memory data will change after occurring the attack behaviors， and benchmark measurement used by the traditional integrity measurement system has the problems of low detection rate and lack of flexibility. Aiming at the above problems， a memory combined feature classification method based on multiple Back Propagation （BP） neural networks was proposed. Firstly， the feature value of the memory data was extracted by Measuring Object Extraction Algorithm （MOEA）. Then， the model was trained by different BP neural networks. Finally， a BP neural network was used to collect the obtained data and calculate the safety status score of the operating system. Experimental results show that compared with the traditional integrity measurement system using benchmark measurement， the proposed method has much higher accuracy and universality， and the proposed method has a detection accuracy of 98.25%， which is higher than those of Convolutional Neural Network （CNN）， K-Nearest Neighbor （KNN） algorithm and single BP neural network， verifying the proposed method can detect attack behaviors more accurately. The proposed method has the model training time about 1/3 of the traditional single BP neural network， and also has the model training speed improved compared with similar models.