Aiming at the problems of low recognition accuracy and low recognition efficiency in the current abnormal flow detection methods of industrial control network, combined with the periodic characteristics of industrial control networks, this study proposes an abnormal flow detection model based on Long-Short Term Memory network (LSTM) time series prediction. This model takes the LSTM network model as the core, and uses the normal historical traffic sequence of the first 15 minutes to predict the traffic data at the next moment. On the premise that the accuracy on the test set is 98.12%, the model’s predicted value can be considered to be normal. By comparing the actual value with the predicted value, it is determined whether there is an abnormality. On the premise of not reducing the recognition accuracy rate, because the predicted value is calculated in advance, this method greatly improves the detection efficiency.