To reduce the time complexity of situational element acquisition and cope with the low detection accuracy of small class samples caused by imbalanced class distribution of attack samples in large-scale networks, a situation element extraction mechanism based on deep auto-encoder network was proposed. In this mechanism, the improved deep auto-encoder network was introduced as basic classifier to identify data type. On the one hand, in the training of the auto-encoder network, the training rule based on Cross Entropy (CE) function and Back Propagation (BP) algorithm was adopted to overcome the shortcoming of slow weights updating by the traditional variance cost function. On the other hand, in the stage of fine-tuning and classification of the deep network, an Active Online Sampling (AOS) algorithm was applied in the classifier to select the samples online for updating the network weights, so as to eliminate redundancy of the total samples, balance the amounts of all sample types, improve the classification accuracy of small class samples. Simulation and analysis results show that the proposed scheme has a good accuracy of situation element extraction and small communication overhead of data transmission.