The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.