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Furthermore, these characteristics entail that resource deployment cannot effectively meet the demands of users for differentiated service quality. Due to this significance, the main objective of this study is to propose an intelligent congestion control strategy based on deep reinforcement learning (ICCDRL) in heterogeneous V2X, which can meet the diverse service needs of vehicles to some extent, so as to solve the problem of network congestion effectively. The proposal is implemented through three aspects: Firstly, the paper establishes a congestion control model based on DRL. Secondly, a large amount of QoS data is used as the training set to optimize the model. Finally, the congestion sensitivity factor is used to select the size of the congestion window for the next moment, resulting in an intelligent congestion control strategy based on QoS on-demand drive. For verification, a series of simulation experiments are designed on the ns-3 simulation platform. 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