Abstract:  When discretizing the continuous attributes of rough sets， the obtained breakpoint set is not the optimal set， resulting in poor discretization effect. Therefore， a particle swarm optimization BP neural network oriented discretization algorithm for continuous attributes of rough sets is proposed. The discretization of continuous attributes of rough sets is analyzed. Particle swarm optimization algorithm is used to improve the weights and thresholds in BP neural network. Based on the optimized BP deep neural network， information systems with continuous attributes are classified， so as to obtain multiple breakpoints， form sub-breakpoint sets， and build candidate breakpoint sets. The candidate breakpoint set is mapped to particles in particle swarm optimization algorithm， and the best breakpoint set is found by changing the speed and position of particles to complete the discretization of continuous attributes of rough set. The experiment results show that the proposed method can better realize the discretization of continuous attributes， and the data consistency is close to 100% when it is the highest， and the classification accuracy and convergence speed are higher under different algorithms， which shows that this method has strong application prospect.

Key words:  , continuous attribute； particle swarm optimization； optimal breakpoint set； rough set； BP neural network； discretization