The study researches indoor thermal comfort from the perspective of smart home, analyzes the thermal comfort evaluation method of PMV, and points out that some of its parameters are difficult to obtain in the smart home scene. The study proposes to introduce the climatic and environmental characteristics to fit the PMV formula while ignoring wind speed and average radiant temperature. The research uses BP neural network algorithm optimized by Differential Evolution (DE-BP) to establish a fitting model, DE algorithm optimizes parameters of neural network, neural network training uses momentum-accelerated stochastic gradient descent algorithm, and adds the normalization layer and L2 regularization of the affine transformation. The test results show that the model is better than the traditional BP neural network in terms of convergence speed, stability, and generalization performance, and can be used within a small error range. It is applied to the system for calculating thermal comfort and reduces the difficulty of input parameters.