An adaptive multi-objective particle swarm optimization(APSO) algorithm based on dynamic analytic hierarchy process(AHP) is proposed. The fuzzy consistent matrix is used to select the global best particle, which ensures the right direction of particle evolution, and the evolution state is measured to adjust the weight and learn coefficients adaptively. Wellknown benchmark functions are used to test the performance of the proposed algorithm, and its diversity and convergence are compared with other algorithms. The results show that the proposed algorithm has better performance in global search, and the diversity and convergence are better as well. At the same time, by applying the proposed method to the challenging PID controller tunning and process model identification of the conversion of methanol to hydrocarbons, the comparison with other algorithms verify the effectiveness and feasibility of the proposed algorithm.