To meet the temperature requirements of a high-sensitivity optical detection module during normal operation and overcome the difficulty of heat dissipation, this paper proposes a sensitivity analysis method that analyzes the parameters that affect the thermal characteristics of the detection module. First, the thermal balance equation of the detection module in the universe is established. According to the design parameters that affect the thermal characteristics of the module, seven temperature-related parameters of the detection module are extracted. The design parameters were then sampled using the Monte Carlo method, and 200 sets of samples were obtained using finite element analysis. Through a back propagation (BP) neural network analysis of the samples, a neural network model between the temperature distribution of the detection module and seven thermal design parameters is obtained. Finally, based on the established neural network model, a BP-Sobol' global sensitivity analysis is performed using the detection module. The analysis results show that the first-order Sobol' sensitivity values of the design parameters X₁ and X₂ are the largest and have the greatest impact on the detection module. The first-order sensitivity and total sensitivity of parameters X₁, X₅, and X₆ vary significantly, and the interaction effect with other parameters has a substantial influence on the detector temperature output. This sensitivity analysis provides a strong guiding role for subsequent thermal design and experiments.