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Web document classification and clustering are two crucial sections in Web data mining. The models, algorithms and simulation experiments for both Web document classification and clustering have been studied separately to support for the personalized services and to overcome the deficiencies and shortcomings of the same type's algorithms in the paper. The Web document classification based on fuzzy reasoning with comprehensive weights and Web search result clustering based on fuzzy logic and neural networks are presented for Web data mining to obtain easily understood, robust and low-priced solutions by exploring the greatest possible extents of imprecision, uncertainty, fuzzy reasoning and partial correctness. The experiments have demonstrated that the established intelligent Web information mining system here makes Web document classification and clustering more accurate, more credible and more rapid than the exciting ones.