Abstract: The flourishing development of e-commerce in China has gradually improved the online review system based on Web 2.0. However, the massive number of online reviews reduces the efficiency of obtaining effective information, leading to the problem of information overload. How to identify helpful reviews for consumers and alleviate the problem of information overload has aroused widespread concern in the industry and academia. Using nearly 50 000 online restaurant reviews from Dianping.com, this paper comprehensively considered the multi-dimensional text features and emotional tendency of reviews, and conducted an empirical study on the influencing factors of review helpfulness based on Tobit regression and negative binomial regression. On this basis, this paper proposed a classification threshold for the helpfulness of Chinese restaurant online reviews, and utilized support vector machine algorithm to examine performance of the threshold. The results show that the number and average length of dish quality dimension attributes of reviews, the number and average length of restaurant service dimension attributes of reviews and positive sentiment tendencies all significantly affect the helpfulness of online reviews. In addition, the model with a threshold of 2 gives the best classification result among all threshold models. Therefore, e-commerce websites can further optimize online review system to improve the efficiency of consumer information acquisition, and promote the continuous development of Chinese online review ecosystem.