With the rapid development of deep recommendation models, Multitask Learning (MTL) enables models to have better generalization ability in the learning process by extracting relevant information from different tasks. Although many studies have focused on extracting task-specific information from expert networks using attention mechanisms, they have failed to fully exploit the rich information among task towers, resulting in the failure of each task to use the information to improve the model's effectiveness. In this paper, design a Cross and Mixture (CM) unit for multitask recommendation to improve the feature exploitation capability of models through feature interaction between task towers. The results show that adding this feature fusion method to the existing multitask model higher the original effect of three common MTL datasets.