Abstract
Workplace accidents pose a serious threat to people’s lives and property and hinder social and economic development. Among these accidents, fire accidents are typical due to their sudden occurrence and severe consequences. To better understand accident evolution laws and improve risk prevention, this study analyzes the characteristics of typical national fire accidents based on 2015–2024 accident statistics. A linear-nonlinear combined Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-LSTM) model is established to predict trends of the number of national overall workplace accidents, deaths, injuries, and direct economic losses, and it is compared with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Seasonal Autoregressive Integrated Moving Average-Long Short-Term Memory (SARIMA-LSTM) models. The results show that the ARIMA-LSTM model integrates the strengths of linear fitting and nonlinear learning, with stronger explanatory power and higher prediction accuracy, as reflected by lower Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values. This study provides technical support for the precise prevention and control of fire accidents, trend prediction of work safety accidents, and helps to establish a scientific and forward-looking safety risk prevention and control system.