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To solve the problem that traditional PSO algorithm is easy to fall into local optimization, a competitive learning-based particle swarm optimization (CLPSO) algorithm is proposed. In CLPSO, first, by dynamically calculating the fitness value of particles, the population is divided into three subgroups: the optimal region, the reasonable region, and the alienated region. Secondly, according to the evolutionary characteristics of the particles in the three subgroups, different updating and variation modes are designed for the three subgroups respectively. Then, 12 benchmark functions are used to verify the performance of the algorithm. The experimental results show that the proposed competitive learning strategy can effectively overcome the premature convergence shortcoming of classical PSO algorithm in dealing with complicated optimization problems. Finally, the CLPSO algorithm was used to optimize the parameters of the fuzzy neural network, and the CLPSO-FNN algorithm was designed, and the soft measurement model of effluent ammonia nitrogen was established. The experiment showed that the CLPSO-FNN soft measurement model could measure the effluent ammonia nitrogen concentration more accurately and in real time.