Chinese Named Entity Recognition (NER) has received extensive research attention in recent years. However, Chinese texts lack delimiters to divide the boundaries of words, and some existing approaches cannot capture the long-distance interdependent features. In this paper, we propose a novel end-to-end model for Chinese NER. A new global word boundary detection approach is designed to capture the semantic dependency via a self-attention mechanism to represent character embedding by assigning compatible weights for each character in a sentence. To improve the representation ability of Chinese named-entity boundaries, we introduce position-aware influence propagation with the Gaussian kernel for each character, which combines convergence propagation and radiation propagation. Convergence propagation mainly measures the influence of surrounding characters on the target character. The purpose of radiation propagation is to measure the range of influence of the target character on surrounding characters. The proposed method has been evaluated and shown to offer strong performance in two Chinese NER datasets: MSRA and PFR.