Concerning low precision and slow speed of traditional intelligent optimization algorithm for parameter identification in chaotic system, a new method of parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm was proposed. This method was based on the teaching-learning-based optimization algorithm, where the feedback stage was introduced at the end of the teaching and learning stage. At the same time the parameter identification problem was converted into a function optimization problem in parameter space. Three-dimensional quadratic autonomous generalized Lorenz system, Jerk system and Sprott-J system were taken as models respectively, intercomparison experiments among particle swarm optimization algorithm, quantum particle swarm optimization algorithm, teaching-learning-based optimization algorithm and feedback teaching-learning-based optimization algorithm were conducted. The identification error of the feedback teaching-learning-based optimization algorithm was zero, meanwhile, the search times was decreased significantly. The simulation results show that the feedback teaching-learning-based optimization algorithm improves the precision and speed of the parameter identification in chaotic system markedly, so the feasibility and effectiveness of the algorithm are well demonstrated.