Aiming at the problems of the traditional disturbance observation method in photovoltaic MPPT control， such as slow response speed and difficulty in maintaining stability at the maximum power point， a hypothesis method was proposed and an adaptive particle swarm optimization algorithm with changing inertia weight and learning factor was proposed for the traditional particle swarm optimization algorithm to achieve global maximum power point tracking. The assumption method mainly assumed the maximum power point through the formula， and improved the step size based on the position of the maximum power point. IPSO algorithm mainly adjusted the parameters of traditional particle swarm optimization algorithm， optimized the search order of particles， and reduced the number of iterations. Through the modeling and simulation of MATLAB/SIMULINK software， the simulation results of the hypothesis method and IPSO algorithm were obtained and compared with the traditional algorithm. The results showed that both the hypothetical method and IPSO algorithm could achieve the accurate control of PV MPPT， which was helpful for the rapid realization of PV MPPT technology and had a good application prospect.