随着卫星通信技术的快速发展，空间信息网络（SINs）已成为支持复杂业务交付和跨域任务协同的重要基础设施，并推动着空间段、地面段、用户段多层协同向意图驱动的任务导向型协作范式转变。本文提出了一种新型意图驱动的任务导向型网络（IDTN）框架，以解决SIN中的任务调度与资源分配难题。该调度问题被建模为一种三方匹配博弈，综合考虑了各网络层实体的偏好属性。为了应对随机任务到达和资源动态变化带来的不确定性，框架引入了上下文感知的线性置信上界（Linear Upper Confidence Bound, LinUCB）在线学习机制，以降低决策不确定性。仿真结果表明，所提出的IDTN框架在性能上优于传统基准方法，实现了系统奖励平均提升4.4%–28.9%，资源利用率提升6.2%–34.5%，用户满意度提升5.6%–35.7%。该框架有望推动空间平台的深度集成与统一编排。

With the rapid advancement of satellite communication technologies, space information networks (SINs) have become essential infrastructure for complex service delivery and cross-domain task coordination, facilitating the transition toward an intent-driven task-oriented coordination paradigm across the space, ground, and user segments. This study presents a novel intent-driven task-oriented network (IDTN) framework to address task scheduling and resource allocation challenges in SINs. The scheduling problem is formulated as a three-sided matching game that incorporates the preference attributes of entities across all network segments. To manage the variability of random task arrivals and dynamic resources, a context–aware linear upper-confidence-bound online learning mechanism is integrated to reduce decision-making uncertainty. Simulation results demonstrate the effectiveness of the proposed IDTN framework. Compared with conventional baseline methods, the framework achieves significant performance improvements, including a 4.4%–28.9% increase in average system reward, a 6.2%–34.5% improvement in resource utilization, and a 5.6%–35.7% enhancement in user satisfaction. The proposed framework is expected to facilitate the integration and orchestration of space-based platforms.