The increasing global trade has facilitated the transfer of ship ballast water, which has emerged as a primary pathway for alien species invasion into marine ecosystems, posing significant threats to marine biodiversity. Addressing the technical challenges in rapid microorganism detection and treatment efficiency assessment, this study developed a confocal Raman microscopic imaging (CRMI) system integrated with a metal-insulator-metal (MIM) broadband surface-enhanced Raman scattering (SERS) chip, enabling efficient acquisition of single-cell Raman spectroscopy (SCRS). By incorporating machine learning algorithms, the system achieved precise identification of up to 10 bacterial types in ballast water, exhibiting remarkable performance metrics with average accuracy, sensitivity, specificity, and precision above 95.5 %, 95.5 %, 99.5 %, and 95.5 %, respectively. To evaluate the efficacy of ultraviolet (UV) treatment, a Raman spectroscopy-based approach combined with heavy water labeling was introduced to characterize the changes in bacterial single-cell metabolic activity under UV 254 irradiation. Experimental results demonstrated that a 10-min UV 254 exposure at an effective intensity of 2 mW/ cm2 was sufficient to achieve complete bacterial sterilization for the specific ballast water used in our experiment. This study not only established an efficient and accurate method for rapid detection of mixed bacteria but also provided a novel perspective for assessing UV treatment effects. It holds significance and practical value for optimizing ship ballast water management strategies and safeguarding the safety of marine ecosystems.