The rapid increase in unmanned aerial vehicle (UAV) usage has introduced significant safety challenges, including issues such as system failure, loss of control, transmission failures, and collisions. Analyzing these incidents has been challenging due to the absence of a dedicated category field in the National Transportation Safety Board (NTSB) data. This research tackles this problem by utilizing artificial intelligence (AI) to automate the classification of UAV accident reports collected between 2006 and 2023. Using natural language processing techniques, we categorize NTSB reports to improve the analysis and interpretation of incident data. We also employ advanced data visualization tools to reveal geographic and temporal patterns, offering a detailed view of UAV accident trends. The results indicate that system and component failures unrelated to propulsion systems (system/component failure or malfunction [non-powerplant]) and abnormal contact upon landing (abnormal runway contact) are predicted as the primary categories (37%) of UAV accidents for the period. These insights suggest the potential value of AI-driven categorization and visualization techniques in enhancing UAV safety standards and supporting policy development. Initial results provide promising insight into the use of language models for text classification in aviation safety problems.