This study explores the online game flow identification based on representation learning. First of all, due to the lack of game flow in the public data set in the field of flow identification, various types of game flow are collected, and a mapping relationship between various games and process ports is established. Depending on the mapping relationship, the game flow is filtered from the collected flow to expand the public data set. Then the representation learning model in deep learning is used to automatically perform feature learning and feature selection on the pre-processed original end-to-end game flow. Finally, the game category is identified by a classifier. The convolutional neural network self-learns the features of the original information via the construction of feature space, successfully avoiding the loss of information caused by the secondary processing of the flow data set in the traditional machine learning algorithm and the dependence of the flow classification model on feature selection. The experimental results show that, compared with the classification effect of the original data set, the expanded data set has a classification accuracy improved by 5% on the neural network model. The accuracy of game flow identification reaches 92%, and the identification performance is significantly improved.