Car networking load balanced access method based on history reinforced learning

The invention provides a car networking load balanced access method based on history reinforced learning. The method comprises the following steps: first of all, obtaining an accessed base station distribution mode of a vehicle via an initial reinforced learning module, and continuously accumulating to an access mode accumulation library; through learning accumulation, enabling a history reinforced learning module to replace the initial reinforced learning module to continuously reside and operate in a system, and when a base station encounters a network change again, invoking, by the history reinforced learning module, a history record in the access mode accumulation library to self-adaptively learn a new vehicle access distribution mode; and recording to form a dynamically changed operating loop of a self-adaptive processing network, thereby guaranteeing that a network load accessed by the vehicle is balanced in a dynamically changed car networking environment. According to the method, a potential regularit