Abstract: In view of the problems of the original Firefly Algorithm (FA), which is easy to fall into local optimization, low solution accuracy and difficult parameter selection of twin support vector machine (TWSVM), a dual support vector machine model (DEFA-TWSVM) based on improved firefly algorithm (DEFA) is proposed. Firstly, the original firefly algorithm is improved to obtain DEFA algorithm. In the firefly position update formula, dynamic inertia weight was combined, and the step size control factor was adjusted adaptively to quickly search for global and local optimal solutions. Differential Evolution (DE) strategy was applied to the firefly population after each movement to ensure the iterative diversity of the population. The simulation results of benchmark test function show that the improved algorithm has strong global optimization ability and is not easy to fall into local optimization. Secondly, DEFA algorithm was used to optimize the parameters of TWSVM. Finally, the classification accuracy of DEFA-TWSVM and other models is obtained by testing in UCI data set. By comparison, it is found that DEFA algorithm can automatically determine TWSVM parameters in the training process, which solves the problem of blind TWSVM parameter selection, and the average classification accuracy is increased by 2 to 5 percentage points compared with other models.