It was successful to denoise the spectrum signal within visual wave band (350-560 nm) by wavelet transformation, to extract the folic acid characteristic wavelength 366 nm and some character wavelengths with relation to chlorophyll at 380, 414, 437 and 554 nm. In the range from 560 to 2 500 nm, after denoising, the biggest error was smaller than 1.47%, while at the peak or vale of character wavelength the biggest error was smaller than 0.11%. Moreover, the model was established based on the denoised data acquired with aid of plant probe. The model was also based on BP neural network and for predicting the nitrogen content in nutrient solution in hydroponic cultivation of tomato. The results showed that the predicting model, which used the values of absorbance at 554, 673, 1 440 and 1 940 nm as input variables of BP neural network, had a very good forecasting accuracy and great potential to be used practically.