The work aims to propose a heat seal temperature control algorithm with parameter adaption based on RBF neural network and PID algorithm for the excessive dependence of parameters of PID control algorithm on artificial experience in traditional heat sealing process. The output error of the temperature control system was used as the cost function, the minimum mean square error (LMS) was applied to adjust the weight and bias, and the dynamic adjustment for the radial basis function center and the center width was realized by the center self-organization algorithm. The simulation model was set up in the Simulink sub-system of the MATLAB software to verify the algorithm and then compared with the traditional PID control algorithm. The simulation results showed that the combination of RBF neural network and traditional PID algorithm made the output response of the system better than the traditional PID in both dynamic performance and static performance and also better than the incremental digital PID in rise time and adjustment time. The RBF neural network PID algorithm is applied to the automatic packing machine to avoid the problem that PID control algorithm parameters cannot adapt to the complex transformation control environment in the traditional heat sealing process. The neural network PID algorithm has strong adaptability and realizes the automatic adjustment of PID parameters under the change of heat seal temperature, so it can improve the production efficiency and the packaging equipment intelligent level to some certain.