In the scenario of Cellular-Vehicle to Everything (C-V2X) based on the evolution of cellular communication, a base station as a Multi-access Edge Computing (MEC) edge cache node can improve the efficiency of user data acquisition, but its cache capacity is limited. Therefore, accurate cache request content prediction in C-V2X has become an important issue that needs to be addressed. This article starts with the time-varying characteristics of file requests and uses the Simulation of Urban Mobility (SUMO) to analyze the traffic flow for actual urban scenario modeling. Secondly, collecting the traffic data of the actual website time-sharing classification, and pre-processing according to the traffic flow rules of each road section, and then the user request model is constructed and the law of the base station receiving data is revealed. The Long Short-Term Memory (LSTM) deep learning model trains and predicts the file requests that each base station will receive. The simulation results show that the root mean square error of the vanillaLSTM model in the entertainment data set is about 1.3 when the data set received by the base station is predicted under the file request formed by NetEase news popularity distribution and request interval distribution.