To address the current limitations in data-driven remaining useful life (RUL) prediction methods for turbofan engines, which suffer from low data utilisation and constrained prediction accuracy due to inadequate exploitation of data feature information, a novel multi-scale RUL prediction model for engines is proposed. This model is termed the distributed spatio-temporal convolutional network (DSCN). The proposed method first captures linear and non-linear relatonships in engine data by calculating Pearson correlation coefficients and maximum information coefficients, thereby obtaining trend features for both stationary and non-stationary time series. Secondly, it employs a multi-scale residual fusion module to enrich data features. Building upon temporal convolutional network (TCN), it incorporates residual channel attention module (Res-CAM) and multi-head attention module (MHAM) to enhance the model′s ability to capture critical information, dynamically adjusting the weights of the data. The proposed method was experimentally validated on the FD001 and FD003 datasets within the C-MAPSS collection, yielding RMSE and Score values of 11.30 and 218.08; 12.04 and 227.65 respectively. Results indicate that this approach reduces the Score by 4.67% and 11.5% compared to the current state-of-the-art method.