Chinese calligraphy has a long and rich history, with hard-pen calligraphy bearing both artistic and practical significance. To address the decline in hard-pen handwriting ability caused by the widespread use of electronic devices, this paper proposes a multi-feature hard-pen calligraphy teaching mode based on force feedback, which integrates font style, stroke order, and writing pressure. Specifically, a Dense-CycleGAN model based on contrastive learning is developed to generate calligraphy font libraries in different styles. The stroke order of Chinese characters is standardized using the Hungarian algorithm. Furthermore, a mapping model from stroke width to writing pressure is constructed based on data collected via force feedback devices. Experimental results on five font styles show that the proposed model achieves an average Structural Similarity Index Measure (SSIM) of 0.587 in character generation, outperforming the traditional CycleGAN. The stroke order standardization yields a Dynamic Time Warping (DTW) score of 0.044 and an average cosine similarity of 0.998, indicating high accuracy. In user evaluation experiments, the writing guidance and teaching assistance received scores of 4.5/5 and 4.1/5, respectively, validating the practicality and applicability of the proposed mode. This writing mode faithfully reproduces the hard-pen calligraphy process and enables instruction that comprehensively considers font style, stroke order, and writing pressure, offering a novel integrated strategy for calligraphy education.