Intelligent manufacturing requires the full use of modern information technology in traditional production scenarios, and assembly line workshops also need intelligent industrial upgrading in production management. To this end, this paper proposes a time series-based evaluation study of the state of equipment in an assembly line workshop. Through the theoretical analysis of the characteristics of assembly line workshop equipment state data, time series data mining technology is used to collect research and analysis data, which is then pre-processed. The differential autoregressive average model is used to construct the assembly line workshop equipment state evaluation model, and the simulation analysis of the assembly line workshop equipment state is carried out by combining the corresponding parameters with the research data. The data shows that the MAPE value of the ARIMA model for equipment failure is −0.0706%, which is more significant compared to the prediction effect of the traditional CNN model. In addition, the error range of the ARIMA model prediction of equipment state parameters of the assembly line workshop is 0.01~0.02. This study can accurately assess the equipment state of the assembly line workshop and lay the foundation for its intelligent transformation.