The space environment is an important part of space situational awareness. In the field of space science, due to the incomplete understanding of the physics laws of the solar-terrestrial system, some existing space environmental prediction models based on physical mechanisms are still difficult to practicalize. It is more common to use the data-based empirical models. The input variable selection(IVS) algorithm based on mutual information(MI) can help to determine the impact inputs of the spatial environmental prediction models. Since taking into account of the potential relationship between different inputs, and between inputs and outputs, the MI-IVS algorithms have been widely applied to the regression and classification problems in recent years. From different perspectives, this paper systematically analyzes the current widely-used filter-based MI-IVS algorithms with the three key points of the IVS algorithms, namely the evaluation criteria, the search strategy and the stopping criterion as a clue. It focuses on the mathematical derivation of the assumptions of these criterias. Finally, the trend of the MI-IVS algorithms is summarized, which can provide reference for subsequent research, especially for establishing space environmental prediction models.