Abstract: A new FPGA hardware implementation approach of dynamic neural network for support vector machines was provided and researched.The structure of dynamic neural network for least square support vector machines (LSSVM) was proposed. The architecture design of dynamic neural network for LSSVM based on VHDL language was also performed. The experiments of classification and regression for LSSVM were achieved on XILINX SPANT3E series FPGA. The experimental results show that it is effective to complete the LSSVM classification and regression based on presented method. Compared with the existing methods based on software implementation or analog device implementation, this approach has better convergence rate and better flexibility.