The invention provides an ISP network abnormal state automatic detection method based on a
deep learning algorithm, and the method comprises the steps: collecting multi-dimensional data of an ISP network, extracting
time sequence features and topological structure features from the multi-dimensional data, splicing the
time sequence features and topological structure features into a fusion
feature vector, inputting the fusion
feature vector into an ISP network state
abnormality monitoring model based on an LSTM-GNN-VAE
hybrid model, and carrying out the automatic detection of the abnormal state of the ISP network in an
encoder. The LSTM sub-network is used for
processing the
time sequence characteristic data to obtain a time sequence coding vector, the GNN sub-network is used for
processing the topological structure characteristics to obtain a topological coding vector, the time sequence coding vector and the topological coding vector are spliced to obtain a fusion vector, the fusion vector is mapped to a
potential space, and a mean value and a variance of potential distribution are output; the decoder generates potential vectors through a re-parameterization technique according to the mean value and variance of the potential distribution, maps the potential vectors back to an original feature space, and reconstructs a comprehensive
feature vector. Through targeted
processing of the multi-dimensional data, the model is optimized in combination with a plurality of set optimization strategies, and the accuracy of ISP network state
anomaly detection is effectively improved.