Abstract: Long Short-Term Memory (LSTM) neural network benefits from its ability to capture long-term dependencies and has shown excellent performance in many practical applications. Multivariable LSTM data-driven prediction model is constructed in this paper to predict Australian forest fires by multivariable input. Firstly, the multivariate LSTM prediction model is used to predict the maximum daily temperature, and the results are compared with those of the back-propagation (BP) neural network and Autoregressive Integrated Moving Average model (ARIMA) prediction model. The results show that the BP neural network with the related variables as input cannot consider the time-series variation law, and the prediction error is the largest. ARIMA with single temperature as input makes corresponding prediction according to time series change, and the prediction effect is good. Multivariable LSTM prediction model comprehensively considers the interaction of many factors, and combines the time series dependence, the prediction effect is the best. Finally, the multivariable LSTM prediction model is used to predict whether a node is on fire, and the prediction results are in good agreement with the actual value. Overall, the multivariable LSTM prediction model is reliable in predicting the Australian fires.