Art therapy plays a vital role in emotional healing, in which narrative creation acts as the primary vehicle for emotional expression. Given the inherently dynamic nature of emotions during healing, narratives with finely controlled emotional fluctuations enable individuals to safely project inner conflicts and achieve emotional catharsis. Recently, with the rapid development of Large Language Models (LLMs), automated narrative generation technology has provided a new pathway to support such artistic designs. However, while existing methods can produce fluent texts, they struggle to generate narratives that adhere to specified affective trajectories, failing to meet the demands of emotion-oriented psychological healing. To address these issues, this paper proposes EC-Script, an LLM agent-based framework that enables hierarchical control of the affective trajectory in narrative generation for emotional healing. To ensure that the generated narratives strictly follow the given emotional patterns, EC-Script establishes overall narrative direction through Emotion-Trajectory Planning, propels scene-level plot development with Character-Driven Scene Generation, and regulates local emotional changes of characters via Emotion-Controlled Script Writing. Ultimately, it outputs scene-by-scene script content that remains highly consistent with the preset affective trajectory. Experimental results demonstrate that EC-Script significantly outperforms baseline methods in affective trajectory adherence, exhibiting excellent and reliable emotional controllability, thereby providing effective technical support for AI-assisted emotional healing scenarios.

翻译：艺术治疗在情绪疗愈中扮演关键角色，叙事创作作为情感表达的主要载体在其中至关重要。由于情绪在疗愈过程中具有天然动态特性，具备精细控制情绪波动的叙事能使个体安全投射内心冲突并实现情感宣泄。近年来，随着大语言模型（LLMs）的快速发展，自动化叙事生成技术为支持此类艺术设计提供了新路径。然而，尽管现有方法能够生成通顺文本，但难以生成符合特定情感轨迹的叙事，无法满足以情绪为导向的心理疗愈需求。针对上述问题，本文提出EC-Script——一种基于LLM代理的框架，能够对情感治愈叙事生成中的情感轨迹实施分层控制。为确保生成的叙事严格遵循给定情感模式，EC-Script通过情感轨迹规划建立整体叙事方向，借助角色驱动场景生成推进场景级情节发展，并通过情绪控制脚本编写调控角色的局部情绪变化，最终输出与预设情感轨迹高度一致的逐场脚本内容。实验结果表明，EC-Script在情感轨迹遵循度上显著优于基线方法，展现出卓越可靠的情感可控性，从而为AI辅助情感疗愈场景提供有效的技术支持。