This paper focuses on the Web-based English-Chinese Out-of-Vocabulary (OOV) term translation pattern, and emphasizes particularly on the selection strategy based on the multi-feature representation for translation evaluation. Three kinds of feature, local feature, global feature and Boolean feature, are extracted from translation candidates based on the fusion strategy of multi-features. By utilizing the CoNLL 2003 corpus for the English Named Entity Recognition (NER) task, the related experiments based on such a standard data source show the promising results. The established multi-feature representation mechanism for English-Chinese OOV term translation model can “filter” the most possible translation candidate with better ability.