With the rapid advancement of large language models, natural-language-based understanding of satellite task requests is becoming increasingly important for improving the accessibility of remote-sensing services. However, satellite commands issued by nonexpert users are often conversational, ambiguous, and terminologically inconsistent, leading to a substantial gap between free-form expressions and structured task representations. To address this challenge, we propose a hierarchy-aware framework for intent recognition and task-oriented text generation from non-expert satellite instructions. Specifically, we design a hierarchical annotation scheme that models intent levels, parameter structures, inter-element relations, and execution complexity, and we further construct a hierarchical sequence representation for learning. We then introduce a boundary-aware sample organization method based on semantic similarity and structural proximity, together with a retrieval-augmented multi-type negative-sample reorganization strategy to enhance robustness. Finally, we adopt Qwen3-8B with LoRA for parameter-efficient domain adaptation and unified generation of top-level intents and task-oriented outputs. Experiments on a manually curated dataset of 4,025 non-expert satellite instructions show that the proposed method consistently outperforms multiple baselines on both intent classification and task-oriented generation, demonstrating a resource-efficient and scalable solution for natural-language satellite task interfaces.