Semisupervisedmakesfulluseoflargeamountsofunlabeledsamplestomakeuptheinsufficiency of labeled samples. Since it is difficult to obtain a large number of accurate labeled samples and it is a good way for modeling by using a small amount of labeled samples or a large number of inaccurate samples, we proposed a new method named as semi-supervised partial least squares ( SS-PLS) to optimize model based on semi supervised learning. We used 211 samples of tobacco near infrared spectrum and sensory evaluation for modeling and used SS-PLS method to optimize tobacco sensory evaluation model. In the optimized model, the coefficient of determination ( R2 ) can reach up to 90%, the ratio of performance to deviation ( RPD) can reach up to 3 . 0 , and the standard error of cross validation and the standard error of prediction ( SECV and SEP) are below 1. 0. We divided the original sensory evaluation and SS-PLS optimized data into three grades of excellent, medium and poor in accordance with the fixed threshold, the result using projection model of based on principal component and Fisher criterion ( PPF ) shows that the classification of SS-PLS optimized data is better than the original sensory evaluation data. The SS-PLS method can solve the data representation problem of using small sample set for modeling and provides a new chemometrics method for near infrared spectroscopy modeling in case of obtaining a large number of accurately labeled samples is difficult.