Due to the learning boom of CFL, the needs of learning equipment increased. However, there is no such tool like e-Rater for TOEFL in the CFL learning field for Chinese teaching instructors and students to use. In this study, we tent to build up an automated essay scoring system for CFL learners leading to a better CFL learning environment. The system we developed in the study used Stanford Parser as a grammar parser to analyze and parse sentences to design some grammar features that could fit the system. We used Bayesian theorem as a machine learning model. By integrating features to the model, we built up a Chinese essay scoring system for CFL. The system could reach to 93% on the adjacent accuracy in rating the scores of essays and could literally use for the practical needs in CFL teaching or test.