For an optimization algorithm, the algorithm structure and the ability of utilizing information obtained in the optimization process are critically important to its performance. Standard particle swarm optimization is conceptually concise and easy to be implemented. However, for every single particle, it can only learn from the best historical experience of itself and the swarm. The experience of the rest particles and state information of optimization process have not been effectively utilized. In addition, the simple iteration mode based on a second order difference equation raises the structural risk of trapping in a local optimum. In order to avoid trapping in a local optimum and the premature phenomenon, we propose an adaptive particle swarm optimization algorithm with perception of swarm activity. Here, the swarm activity is defined as the current searching state of the algorithm. According to the swarm activity, typologies and searching modes of particles are adaptively changed, enhancing the ability of global convergence of the particle swarm in some extent. Simulation of some Benchmark functions demonstrate the effectiveness and features of the proposed algorithm.