Dialogue generation is a critical research area in human-machine interaction, playing a fundamental role in developing dialogue systems. However, current approaches frequently struggle to capture key entities and suffer from a lack of syntactic diversity, especially when applied to a complex scenario. To address these issues, a novel learning framework is proposed, which is called Context-Aware Training with Clue-Guided Prompt Reasoning for Task-Oriented Dialogue (CAT-CPR-TOD). During the training stage, an adaptive context-aware optimization strategy is introduced in CAT-CPR-TOD, leveraging the syntactic characteristics of dialogue responses. This strategy employs an implicit context gating mechanism to effectively capture both core semantic information and the syntactic structure of the dialogue. As for the reasoning stage, a clue-guided example optimized prompt generation approach is used to construct a database of example prompts by retrieving semantically related examples, generating contextual summaries and merging core semantic information. Clue prompts are generated to guide the contextual fine-tuning model Context-Aware Clue-Guided Prompt Language Model toward producing diverse and contextually relevant dialogue responses. Experimental results demonstrate the effectiveness of the proposed framework. On the MultiWOZ 2.1 dataset, CAT-CPR-TOD is shown to outperform models such as SimpleTOD-Inter and LLaMA2-7b-chat, with improvements of at least 2.93%, 4.5%, and 3.51% in the metrics of inform, success, and BLEU, respectively.