This paper explores online learning model predictive control (MPC) for switched systems with stable terminal constraints. In order to reduce the computational burden, we decompose the infinite horizon MPC into n finite horizons, and adopt adaptive dynamic programming (ADP) to assist MPC in online solving the optimal control problem in each finite horizon. Further, we use a set of stable terminal constraints to ensure both the convergence of online learning MPC in each finite horizon and the connection of adjacent finite horizons. In addition, in order to ensure the uniform ultimate boundedness (UUB) of the triggered switched systems, on the one hand, a novel performance dependent mixed switching law (PD-MSL) is proposed to both avoid frequent switching and take advantage of performance dependent decision-making; on the other hand, an analytical framework of the coupling between mode switching and event-triggering is proposed. Finally, the validity of the presented approach is demonstrated through simulations.