While traditional methods for solving turbulence problems are computationally intensive, machine learning-based models show potential promise in the field of turbulence modeling, but are currently mainly limited to specific flow fields. To this end, a deep learning-based turbulent kinetic energy dissipation rate prediction model for helical flow fields is proposed in this paper. Firstly, the dataset is obtained by numerical simulation of centrifugal fan flow field, then the input features are selected according to the theoretical model of turbulent kinetic energy dissipation and operating conditions, the dissipation rate prediction model is built and trained using the neural network algorithm, and finally, the model is tested for the centrifugal fan flow field in the untrained region and at different rotational speeds, and it is applied to the flow field of the decanter centrifuge and the channel flow field. The results show that the constructed models exhibit good dissipation rate prediction ability in different regions and speeds of centrifugal fan flow fields, with the coefficient of determination (R2) above 0.9, and can accurately predict the trend of turbulent dissipation in the decanter centrifuge flow field and the channel flow field. This study provides new ideas for solving complex flow field turbulence problems, and also provides a reference for the application of machine learning in flow field research.