Online handwritten Chinese character generation is an inter esting task which has gained more and more attention in recent years. Most of the previous methods are based on autoregressive models, where the trajectory points of characters are generated sequentially. However, this often makes it difficult to capture the global structure of the hand writing data. In this paper, we propose a novel generative model, named Diff-Writer, which can not only generate the specified Chinese charac ters in a non-autoregressive manner but also imitate the calligraphy style given a few style reference samples. Specifically, Diff-Writer is based on conditional Denoising Diffusion Probabilistic Models (DDPM) and con sists of three modules: character embedding dictionary, style encoder, and an LSTM denoiser. The character embedding dictionary and the style encoder are adopted to model the content information and the style information respectively. The denoiser iteratively generates char acters using the content and style codes. Extensive experiments on a popular dataset (CASIA-OLHWDB) show that our model is capable of generating highly realistic and stylized Chinese characters