关键词: 深度学习, 深度信念网络, 物流效率, 自适应, 聚类

Abstract: The paper deeply studies the researches of urban logistics efficiency, constructs a continuous deep belief network with three hidden layers （3CDBN） combining with the relevant theory of deep learning, defines the knowledge set of 3CDBN, proposes the adapted DBN algorithm and proves its convergence. And then, this paper verifies the pattern classification ability of the network and algorithm using the Iris data set and the Wine data set. The classification accuracy of the 3CDBN is better than 2CDBN and Error Back Propagation Neural Network （EBPNN）. At last, according to the logistics characteristics of cities along New Silk Road, 20 core cities are chosen as research objects, adaptive DBN algorithm and Social Network Analysis （SNA） are used to carry on cluster analysis at the target of logistics efficiency evaluation, which with 4 dimensions and 13 indicators. The research results show that the adaptive DBN algorithm is more effective, which lays the foundation for determining the urban logistics strategy of cities along the New Silk Road and promoting the future cooperation and development of the domestic logistics industry.

Key words: deep learning, deep belief network, logistics efficiency, adaptive, clustering