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This paper presents a new keyword extraction algorithm for Chinese news Web pages using lexical chains and word co-occurrence combined with frequency features, cohesion features, and corelation features. A lexical chain is an external performance consistency by semantically related words of a text, and is the representation of the semantic content of a portion of the text. Word co-occurrence distribution is an important statistical model widely used in natural language processing that reflects the correlation of the words. Lexical chains and word co-occurrence are combined in this paper to extract keywords for Chinese news Web pages in our proposed algorithm KELCC. This algorithm is not domain-specific and can be applied to a single Web page without corpus. Experiments on randomly selected Web pages have been performed to demonstrate the quality of the keywords extracted by our proposed algorithm.