This paper presents a comprehensive strategy and analysis for constructing dialect speech data for middle-aged and elderly populations aimed at enhancing artificial intelligence (AI) training. Recognizing the critical role of high-quality, diverse speech datasets in improving AI's real-world performance, especially in speech recognition, this study focuses on the underrepresented dialects of older demographics. It outlines the methodologies employed in collecting, processing, and labeling the speech data, ensuring the inclusion of various dialectical nuances, intents, and emotional states. Additionally, the paper discusses the project's challenges, including ensuring data diversity and the technical aspects of data processing. By addressing these areas, the research contributes to the development of AI systems better attuned to the linguistic diversity and needs of older users, potentially improving AI accessibility and user experience across different applications.