To maximize the accuracy of the news headline generation model, increase the attention ratio of the model to significant information, and avoid duplication of generated headlines and problems unrelated to feature semantics, we proposed a topic inference Chinese news headline generation method integrating a copy mechanism (TI-C-NHG). First, we enrich the TI-C-NHG input module to mine potential new topics through topic reasoning, making topic understanding a broader source of sentence-level context. Second, we propose a copy mechanism that can copy words from a vocabulary and news texts with topic information, which helps to improve the accuracy and readability of headings. In addition, the training model constructed by a multi-layer Transformer-Decoder can greatly improve the parallel ability of the model and speed up the inference process of headline generation. We verified the validity of TI-C-NHG in the Chinese Short Text Summary Datasets and the LCSTS datasets.