The work aims to optimize the curing process of graphene/carbon nanotube composite electric heating film by response surface method and neural network genetic algorithm and compare the optimization results of the two methods, so as to provide the optimal process parameters for preparing the composite electric heating film. The effects of slurry weight, curing temperature and curing time on the volume resistivity of composite electric heating film were discussed through single factor experiments. On this basis, the BB test design was carried out, and the response surface method (RSM) and BP neural network were analyzed and optimized based on the BB test results. The single factor experiment results showed that with the increase of the weight of the electric heating film, the volume resistance firstly decreased and then increased. With the increase of the curing temperature or the curing time, the volume resistance gradually decreased until it became stable. Experimental verification was conducted on the optimal curing process obtained by optimized BB response surface method and GA-BP genetic neural network method. The relative error of the optimized GA-BP genetic neural network model was relatively small at 1.06%, so the optimal curing process parameters were weight of 0.056 g/cm2, curing temperature of 85.71 ℃ and curing time of 11.13 h. The research results had a reference value for the preparation process of graphene/carbon nanotube composite electric heating film. The response surface analysis of variance shows that the three factors of weight, curing temperature, and curing time have significant linear and square effects on the volume resistivity. BP neural network prediction model has good accuracy and can be used to predict the volume resistivity of graphene/carbon nanotube composite electric heating film.