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The Particle swarm optimizer is intelligent searching in space to find the optimal solutions through the cooperation and competition between particles, being based on the theory of swarm intelligence global optimization algorithm. Its advantage is that simple operation and easy achievement. In this paper, a new algorithm PSO- BP was studied, giving full play to both of the particle swarm algorithm of global optimization ability and BP algorithm's local search advantage, and compared identification of 140 pixels of English letters together with BP algorithm. Experimental results show that particle swarm algorithm used for the optimization of the neural network has a faster convergence speed, and simpler algorithm.