Weakly supervised video anomaly detection has gained attention for its effective performance and cost-efficient annotation, using video-level labels to distinguish between normal and abnormal patterns. However, challenges arise from the diversity and incompleteness of anomalous events, complicating feature learning. Vision-language models offer promising approaches, but designing precise prompts remains difficult. This is because accommodating the diverse range of normal and anomalous scenarios in real-world settings is challenging, and the workload is significant. To tackle these issues, we propose integrating multilingualism and multiple prompts to improve feature learning. By utilizing prompts in various languages to define "anomaly" and "normalcy," we tackle these concepts across different linguistic domains. In each domain, multiple prompts are employed for adaptive top-K prompt selection of snippets. To enhance visual feature learning, a multi-granularity attention module combining Transformer and Mamba is designed. Mamba's long-range adaptation selection builds fine-grained temporal correlations among coarse-grained snippets, while Transformer enhances fine-grained information guided by coarse-grained information. Alongside a multilingual prompt guidance loss, we introduce a gradual directional loss to jointly optimize visual feature distribution and the top-K prompt selection. Our method demonstrates effectiveness on four video datasets and provides generalizability analyses on two medical datasets, including EMG and ECG temporal data.