Customs anti-smuggling intelligence as an important link in the anti-smuggling chain. From the massive case information in time to find effective intelligence clues, all kinds of anti-smuggling case data for automated intelligence extraction, high efficiency according to the case of time, location and other elements of the case thread; anti-smuggling case intelligence under the environment of big data for intelligent correlation, anti-smuggling cases in the intelligence of intelligent research and analysis. This is especially important for the construction of China’s public security informatization in recent years, through big data technology, entity and attribute as nodes, relationship as the edge, the establishment of a semantic network based on the structure of the knowledge graph, through the integration of a large number of anti-smuggling intelligence knowledge graph structure, and further to realize the semantic relationship network of potential criminal individuals or groups predicted to stimulate the value of the existing public security big data to create a combination of the experience of public security business A knowledge graph-based anti-smuggling intelligence analysis method.This paper introduces the following aspects: firstly, we construct the spatio-temporal based expression of anti-smuggling intelligence elements to realize the logical relationship representation of anti-smuggling cases. At the same time, we construct an information extraction model combining deep learning and conditional random field driven by elements of anti-smuggling intelligence under big data environment. In addition, we further design and propose the association method of anti-smuggling intelligence based on graph convolutional neural network, so as to construct the three-dimensional anti-smuggling intelligence research and judgment model based on space and time.