Conditional probability neural network (CPNN) has special advantage in pattern classification problems.However, how to find the optimal parameters of the CPNN to achieve better performance is an extraordinary challenge.Considering the structure feature of CPNN, we proposed a new training method based on particle swarm optimization (PSO).This method utilizes PSO to optimize the structure of CPNN and label distributions by introducing Hellinger distance between different label distributions.We applied the improved CPNN on facial age estimation.The experimental results showed that this network could increase recognition accuracy significantly.