Most current recommendation models often overlook the importance of features during feature interactions, leading to low accuracy. To address this issue, an enhanced recommendation model combining feature selection and the cross network is proposed. The SENet network is employed to filter out unimportant features before feature interaction, enabling the extraction of more valuable interaction information. On this basis, parallel cross network and deep neural network are utilized to capture explicit and implicit feature interactions. Additionally, low-rank techniques are introduced in the cross network, transforming weight vectors into low-rank matrices to maintain model performance and reduce model training costs. Comparative experiments on the datasets of MovieLens-1M and Criteo demonstrate that the proposed recommendation model is significantly superior to other models in terms of AUC metrics, which proves the effectiveness of the proposed recommendation model.