Aircraft turbulence is one of the critical aviation weather hazards affecting flight safety and passenger comfort. Therefore, accurate predictions of aircraft turbulence are particularly essential. Although numerical weather prediction (NWP) and artificial intelligence methods have been applied in recent years for turbulence research and commercial applications, these approaches have not fully addressed the dynamic nature of turbulence across temporal and spatial changes, making it challenging to handle small-scale turbulence that is complex and variable. To address this, energy dissipation rate (EDR) data was used to develop a deep learning model, CNN-Transformer-BiLSTM (CNTFBL), aimed at turbulence prediction for aircraft. Comparison and ablation experiments show that, compared to traditional machine learning methods, this model leverages CNN to capture small-scale, localized spatial turbulence features, Transformer to capture long-term global dependencies, and BiLSTM to manage short-term local dependencies. These combined advantages significantly enhance accuracy in small-scale and short-term moderate turbulence predictions. Notably, the model performs exceptionally well at critical forecast times, achieving accuracy rates of 0.950 3 and 0.953 5 for turbulence predictions at 9 h and 12 h in advance, respectively.