It is difficult to establish an accurate mathematical model of energy consumption for the temperature variation of a resistance furnace due to its nonlinear and large delay characteristics. In order to solve the problem of complexity and not real-time performance of theoretical modeling, a data driven based multi-parameter energy consumption prediction approach of the resistance furnace is developed in this paper. Firstly, the theoretical energy consumption prediction model of the resistance furnace is established by analyzing the energy consumption characteristics of the resistance furnace in the working stage. Then, the particle swarm optimization algorithm is used to optimize the hyper-parameters of support vector regression, and a multi-parameter energy consumption prediction model based on support vector regression is established. Finally, the energy consumption prediction results of support vector regression, gaussian process regression, and adaptive network-based fuzzy inference system models under single parameter and multi-parameter conditions are compared. The experimental results show that the support vector regression multi-parameter energy consumption prediction method based on particle swarm optimization has better prediction effect.