Research on Tourism English Translation System Based on Fuzzy Clustering Algorithm

Abstract

The emergence of economic globalization and the Internet have precipitated extensive and profound international exchanges and collaborations. Language barriers have surfaced as the primary impediment to effective international communication and cooperation. This study is dedicated to the development of a tourism English translation system utilizing the fuzzy clustering algorithm. The system's functionalities undergo testing and validation through black box testing. Key performance indicators like response time and throughput are scrutinized to evaluate the system's effectiveness. The performance evaluation involves monitoring specific checkpoints in test cases to ascertain whether the system aligns with the essential performance criteria. The system's response time and throughput take the forefront in this educational system assessment. Use-cases are categorized based on the number of online users, with the client's response time being assessed in each scenario. Successful completion of the criteria deems a use case qualified; conversely, failure designates it as unqualified, with any system deficiencies recorded within the testing framework. Data analysis reveals that the integration of the enhanced PCM algorithm elevates C-FCA's clustering data accuracy to 95%. Consequently, the findings signify that the fuzzy clustering algorithm significantly amplifies the precision of the tourism English translation system.