The design of regenerative braking strategies requires a comprehensive consideration of multiple factors, among which vehicle driving conditions, driver characteristics and the road surface on which the vehicle is traveling have a significant impact on the regenerative braking process. In order to formulate regenerative braking strategies for electric vehicles that are adaptable to various driving conditions, improve the vehicle braking energy recovery rate and maintain braking stability, a regenerative braking strategy that comprehensively considers the influences of driving cycles, drivers and road information is proposed. Firstly, a simulation driving platform is set up to conduct driver-in-the-loop experiments and collect driving data from different drivers, thereby extracting feature parameters of driving conditions and driving styles. Then, a support vector machine (SVM) is used to train the models for identifying driving conditions and driving styles. Secondly, a road image dataset is established and a semantic segmentation network is used for road image preprocessing to remove the complex background information of the image and thereby improve the recognition efficiency. Then, a lightweight neural network, MobileNet V3, is adopted to train the road recognition model. Finally, the regenerative braking strategy base on this is formulated. The front and rear braking force distribution is optimized considering the road adhesion conditions, and a regenerative braking force correction method that takes driving cycles, driver and road information as weight factors is put forward. The simulation results show that the proposed regenerative braking strategy can take into account different driving cycles, drivers and road conditions, and further improve the vehicle energy recovery rate and braking stability.