This paper presents our implementation techniques for an intelligent Web image search engine. A reference architecture of the system is provided and addressed in this paper. The system includes several components such as a crawler, a preprocessor, a semantic extractor, an indexer, a knowledge learner and a query engine. The crawler traverses web sites in multithread accesses model. And it can dynamically control its access load to a Web server based on the corresponding capacity of the local system. The preprocessor is used to clean and normalize the information resource downloaded from Web sites. In this process, stop-word removing and word stemming are applied to the raw resources. The semantic extractor derives Web image semantics by partitioning combining the associated text. The indexer of the system creates and maintains inverted indices with relational model. Our knowledge learner is designed to automatically acquire knowledge from users’ query activities. Finally, the query engine delivers search results in two phases in order to mine out the users’ feedbacks.