Abstract: [Objective] This study was completed to provide timely and accurate forecast to the government and tourists, guide rural tourism activities. [Method]Two different prediction models , the effective accumulated temperature rule based on the ForcTT model and the stepwise regression, were established based on the observation data of rape growth period and ground meteorological observation data from 2004 to 2015. [Result] The results showed that: The effective accumulated temperature rule based on the ForcTT model effectively avoided the uncertainty of each growth period in the law of effective accumulated temperature. The results of the two prediction models were approximate to the prediction of florescence for the most recent three years, which were biased from the actual value, but ccould accurately reflect the changes of the florescence of the years before and after. The results showed that the accumulated temperature model could reflect the time difference of flowering period in different regions effectively. [Conclusion]Therefore, the effective accumulated temperature law based on the ForcTT model could serve as an effective means for the prediction of rape flower period and provide technical support for the development of more effective tourism meteorological services.