An improved zi2zi generative adversarial network for generating calligraphic characters was proposed to solve the problems of missing strokes, wrong glyph structure, blurred images and poor quality in generating fonts by generative adversarial networks. The residual block with a convolution kernel of 1 was introduced into the encoder to improve the ability of the generator to extract detailed features of calligraphic fonts. The context-aware attention structure was increased to extract the stylistic features of calligraphic fonts. The spectral normalization was used in the discriminator to enhance model’s stability, which avoided pattern collapse due to unstable model training. The minimum absolute error L₁ norm was used to constraint the font edge features, which made the font outline clearer. Then two styles of calligraphy characters were generated. The test results of the target style datasets of Yan Zhenqing Regular Script and Zhao Mengfu Running Script showed that the subjective and objective evaluation results of the proposed algorithm were better than the comparison algorithm compared with zi2zi. The peak signal-to-noise ratio was increased by 1.58 and 1.76 dB respectively, the structural similarity was increased by 5.66% and 6.91% respectively, and the perceptual similarity was reduced by 4.21% and 6.20% respectively.