Chinese paleography, the study of ancient Chinese writing, is undergoing a computational turn powered by artificial intelligence. This position paper charts the trajectory of this emerging field, arguing that it is evolving from automating isolated visual tasks to creating integrated digital ecosystems for scholarly research. We first map the landscape of digital resources, analyzing critical datasets for oracle bone, bronze, and bamboo slip scripts. The core of our analysis follows the field's methodological pipeline: from foundational visual processing (image restoration, character recognition), through contextual analysis (artifact rejoining, dating), to the advanced reasoning required for automated decipherment and human-AI collaboration. We examine the technological shift from classical computer vision to modern deep learning paradigms, including transformers and large multimodal models. Finally, we synthesize the field's core challenges -- notably data scarcity and a disconnect between current AI capabilities and the holistic nature of humanistic inquiry -- and advocate for a future research agenda focused on creating multimodal, few-shot, and human-centric systems to augment scholarly expertise.

翻译：中国古文字学，即对古代汉字书写体系的研究，正在人工智能的推动下经历一场计算转向。本立场论文描绘了这一新兴领域的发展轨迹，论证其正从自动化孤立的视觉任务，向构建服务于学术研究的集成化数字生态系统演进。我们首先梳理了数字资源格局，分析了甲骨文、金文及简帛文字的关键数据集。分析的核心沿循该领域的方法论流程展开：从基础视觉处理（图像修复、字符识别），到上下文分析（器物缀合、断代），直至自动化释读与人机协同所需的高级推理。我们审视了从经典计算机视觉到现代深度学习范式（包括Transformer架构与大型多模态模型）的技术变迁。最后，我们综合论述了该领域的核心挑战——尤其是数据稀缺性以及当前人工智能能力与人文学科整体性研究范式之间的脱节——并倡导未来的研究议程应聚焦于构建多模态、小样本、以人为中心的系统，以增强学者的专业研究能力。