To solve the problem of difficulty in balancing trajectory optimization and real-time performance in lane change and obstacle avoidance trajectory planning for intelligent vehicles, a CBR mechanism based intelligent vehicle lane changing and obstacle avoidance trajectory planning method was proposed. The optimal trajectories in typical scenarios were obtained by offline optimization of the fifth-degree polynomial trajectory parameters, forming an optimal case library consisting of the state variables and decision variables. The optimal trajectory correction rule in the new case scenario was explored, and a trajectory key parameter update function was established. Thus, existing case trajectories were directly called in similar case scenarios, and the lane changing and obstacle avoidance trajectories were updated according to state variables such as vehicle speed and position in new case scenarios, thus the case library was updated. The comparative test results show that the trajectory performance of proposed method is basically consistent with that of the fifth-degree polynomial online optimization method, and is significantly better than that of A^* and Rapidly-exploring Random Trees-star(RRT^*) methods. Specifically, the maximum curvature of the trajectories from the proposed method and the fifth-degree polynomial online optimization method are both 0.001 2 m^-1, and the maximum lateral acceleration of the trajectories from the two methods are both 1.245 7 m/s² in the similar case scenario. While in the new case scenario, the maximum curvature of the trajectories from the two methods are both 0.001 4 m^-1, and the maximum lateral acceleration is 1.194 9 and 1.1365 m/s², respectively. Meanwhile, compared with the three comparative methods of the fifth-degree polynomial online optimization, A^* and RRT^*, the proposed method has the smallest planning time. The planning time in the new case scenario and the similar case is only 0.001 1 and 0.000 1 s, respectively.