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In order to solve the problem of low prediction accuracy due to the influence of random factors such as building inertia and personnel in human thermal comfort in rural towns on the prediction results during short-term prediction within the test day, we propose an optimized Long Short-Term Memory Neural Network (LSTM) based on the Improved Sparrow Search Algorithm (ISSA). A new short-term prediction model for thermal comfort of residential air conditioners is developed based on the Long Short-Term Memory Neural Network (LSTM) method. Firstly, we analyzed the dynamics of the weather data on the test days, verified the validity of the data and constructed various thermal comfort prediction models; then, we selected the new household thermal comfort short-term prediction model (ISSA-LSTM) to predict thermal comfort. The results showed that the highest prediction mean squared error (MSE) of the model was 0.02296 and 0.10827 higher than that of the Sparrow Search Algorithm (SSA) and Dung beetle optimizer (DBO) optimized LSTM, respectively. The ISSA-LSTM method improves the accuracy problem of short-term thermal comfort prediction and improves the performance of split air conditioners to control temperature through thermal comfort.