Nowadays, many supervised learning techniques have been applied to requirements traceability recovery (RTR). However, the performance of these supervised learning techniques is still far from satisfactory, and exploring a more effective model is necessary. This paper proposes a new deep forest model for RTR(DF4RT) with a novel composition to improve the model's performance. The proposed model incorporates three feature representation methods, which not only information retrieval and query quality but also add distance. The DF4RT model is evaluated on four open-source projects and compared with nine state-of-the-art tracing approaches. The experimental results show that DF4RT improves precision by 94%, recall by 58%, and F-measure by 72% on average. We also conduct ablation experiments to explore the impact of the different features. It is the first time that deep forest is employed in requirements traceability. Our approach is effective for RTR with good interpretability, few parameters, and good performance in small-scale data.