In the context of the large-scale deployment of 5G base stations, atmospheric ducts cause remote interference in time division duplex systems. Addressing the impact of remote interference on communication systems necessitates timely prediction and emergency mitigation of atmospheric ducts. In this paper, a genetic programming-based multiclass remote interference prediction network model is proposed. Firstly, the proposed model can directly learn to make predictions from extensive databases without relying on any assumptions. Secondly, it presents a genetic programming strategy capable of automatically adjusting the model's structure, thereby enhancing the prediction accuracy of various interference classes. Numerical results demonstrate that the multiclass remote interference prediction network (MRIPNet) outperforms state-of-the-art interference prediction models when tested on real-world datasets. Further-more, MRIPNet excels in accurately predicting a small number of severe interference, which help operators promptly execute interference avoidance measures.