In this paper we present a hybrid approach for automatic classification of Chinese unknown verbs. The first method of the hybrid approach utilizes a set of morphological rules summarized from the training data, i.e. the set of compound verbs extracted from Sinica corpus, to determine the category of an unknown compound verb. If the morphological rules are not applicable, then the instance-based categorization using the k-nearest neighbor method for the classification is employed. It was observed that some suffix morphemes are frequently occurred in compound verbs and also uniquely determine the syntactic categories of the resultant compound verbs. By processing and calculating the training data, 15 suffix rules with coverage over 2% and category prediction accuracy higher than 80% were derived. In addition to the above type of morphological rules, the reduplication rules are also useful for category prediction, such as some famous Chinese reduplication rules, like “aa” in two characters word, “aab”, “abb” and “aab” in three characters word etc. For instance,“喝喝茶”has the same category as “喝茶,” and “研究研究” has the same category as“研究.” As a result, nine reduplication patterns are generated. Experimenting on the training data, it is found that the overall accuracy of the morphological rule classifier is 91.67% and its coverage is 23.19% only. Since the coverage of the morphological rule classifier is low, an instance-based categorization method is employed to taking care the uncovered cases. The instance-based categorization utilizes similar examples to predict the category of an unknown verb. The lexical similarity was measured by both the semantic similarity and syntactic similarity. The semantic similarity between two words is measured by the semantic distance of their HowNet definitions and the syntactic similarity is measured by the distance of their syntactic categories. The distance between two syntactic categories is their cosine measure of their grammatical feature vectors derived from the Sinica Treebank. The category of an unknownverb is predicted as the same as the examples, which are most similar to the unknown verb according to the above criteria of the similarity. For testing on the training data, the optimal accuracy of instance-based categorization is 71.05%, when the similar examples are from unknown verbs and verbs in the dictionary (known verbs). Both the morphological rule classifier and the instance-based categorization have the advantages of not only predicting the syntactic categories of the unknown words but also recognizing their morphological structures and major semantic classes. The advantage of the morphological rule classifier is its higher accuracy and for the instance-based categorization is its higher coverage. However, both of the methods have their own drawback; the former cannot be applied to most unknown verbs, but the latter suffers from low accuracy. For open test, 1000 unknown verbs that are unseen in the training process were tested. The accuracy of the linguistic rule is 87.25%, and the instance-based categorization is 65.04%. Finally, the overall accuracy of the hybrid approach is 70.80%.