推荐技术中的单领域推荐算法面临诸多现实问题，主流的协同过滤算法只利用了用户与物品的交互的信息，无法避免会受到实际应用场景中数据稀疏性的影响。本文致力于融合其他领域的信息来处理稀疏数据，解决了现有算法较难从跨领域中找到有效关联的问题。文中提出了一种联想型感知网络模型，它基于深度神经网络，通过深度挖掘物品的内容信息和信息跨领域的方法来实现迁移学习，获取最优化的特征关联，进而优化了推荐的准确性。该算法很好地处理了推荐系统中数据稀疏性问题，并且在公测数据集的实验中比多种新近算法提升达到 15% ～20%，表现出了更好的性能和可扩展性。

In recommendation system，the single-domain recommendation algorithm faces many practical prob-lems． The main collaborative filtering algorithms only use the information of the interaction between users and i-tems，and can not avoid the problem of data sparsity in practical applications． This study focuses on the fusion of information from other fields to deal with sparse data，and solve the problem that it is difficult to find effective asso-ciation from cross-fields． An associative feeling network model based on the deep neural network was proposed． It realizes transfer learning by mining the content information and information of items to obtain the optimal feature as-sociation，thus the accuracy of recommendation was optimized． The algorithm can deal with the problem of data sparsity in the recommendation system． Compared with some recently proposed algorithms，it shows an improved performance (15% ～20%) and better expansibility．