As a major category of plant dyeing, tea dyeing has a deep cultural heritage while having good environmental protection performance. In order to accurately describe the spectral changes of tea staining, this work studied the relationship between the spectral reflectance of rice paper dyed with tea and the tea concentration. First, a spectrophotometer was used to measure the spectral reflectance of rice paper in the 400 to 700 nm band which was stained by tea leaves. Prediction models were constructed by the spectral reflectance of rice paper and tea concentration based on the partial least squares regression model, BP neural network and continuous projection algorithm(SPA) selected feature band, respectively. Then the spectral reflectance was used as an input variable to predict the tea concentration. The results show that the partial least squares method, BP neural network and continuous projection algorithm select characteristic bands to establish a model to predict tea concentration through the spectral reflectance of tea dyed rice paper, which has high robustness and reliability. SPA-BP neural network model has the best performance: the average prediction accuracy rate is 98.40%, the coefficient of determination is 0.9910, and the root mean square error is 0.843 3. This shows that it is feasible to predict tea concentration through spectral data of tea dyed rice paper.