Network theory can be used to analyze specific attribute groups. Using the executives' resumes of top Internet enterprises as data source, defining executives' name and entity keywords extracted by a word segmentation system as nodes and constructing links between the executive and keyword, when the keyword is in his resume, a network was built. Based on the network theory and the presented model, complex network characteristics were explored and analyzed. The experiment results show that the degree values of some keywords are significantly different, meanwhile normalized eigenvectors are observably larger than the normalized betweeness centrality. The statistical analysis prove that the United States and Beijing related backgrounds are extremely important for executives of top Internet enterprises, and the comparison between the normalized betweenness and eigenvector demonstrates that entities that individuals connect to in social experience are more important than the position of those individuals in social networks, the career experience of cross-industry job-hopping may not conducive to individuals' developments in a new enterprise.