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Alzheimer's Disease (AD) poses a growing global health challenge due to population aging. Using spontaneous speech for the early diagnosis of AD has emerged as a notable area of research. In response to the global trend of AD, our study proposes a speech-based multilingual AD detection method. In our study, we utilize Whisper for transfer learning to build a multilingual pre-trained AD diagnostic model that achieves 81.38% accuracy on a test set comprising multiple languages. To enhance low-resource language performance, we fine-tune the pre-trained model with multilingual data and full transcripts as prompts, achieving a 4-7% accuracy improvement. Additionally, we incorporate the speaker's background information, enhancing the accuracy of low-resource languages by 11-13%. The results demonstrate the validity of our work in multilingual Alzheimer's detection tasks and also illustrate the feasibility of our approach in addressing the global need for Alzheimer's detection.