The accuracy of chassis control for intelligent electric vehicles (IEVs), especially in road-based IEVs control for improving road holding and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate road-based control algorithms, how to precisely and effectively choose a reasonable road-based control algorithm become a hot topic in both academia and industry. To address and improve the performance of road holding and ride comfort of IEVs by using a semi-active suspension system, an adaptive sliding mode control (ASMC) algorithm-based road information is proposed to realize the overall performance of the intelligent vehicle chassis system in the paper. Firstly, the models of road excitation and equivalent hybrid control of a quarter semi-active suspension system are established. Secondly, connecting with the minimum redundancy maximum relevance (MRMR) approach and probability neural network (PNN) theory, the method of road classification is developed based on the MRMR-PNN algorithm under various road excitation. Thirdly, using the sliding mode variable structure and neural network control theory, an ASMC algorithm based road information is developed. Then, a cuckoo search-based multi-objective optimization method is employed to obtain the optimization control parameters of the proposed ASMC algorithm. Finally, compared with the passive suspension system, the skyhook control algorithm, and the ASMC algorithm by simulation and test, the performance indexes of road holding and ride comfort are analyzed under ISO-C road excitation. Simulation and experimental results show that the better performance of the proposed ASMC algorithm can be obtained under different control weights of semi-active suspension system performance indexes, but also the root mean square values of sprung mass acceleration and rattle space compared with passive suspension system optimize no less than 6%. The research achievements develop a reasonable algorithm to apply to improving chassis performance for electric vehicles.