110 air-dried soil samples were collected from Urumqi，Xinjiang. The selected soil samples were sieved at 2.00，0.50 and 0.25 mm，respectively. The reflectance spectral data were collected indoors，and the abnormal values were removed from the collected spectral data (R) and soil nutrient content data. Then the breakpoint correction and ten spectral reflectance transformations were carried out on the spectral data，and the log-first derivative (lgR)' with the highest correlation with the available phosphorus content was selected and transformed. On this basis，the competitive adaptive weighting algorithm (CARS) was used to screen the characteristic bands of the processed (lgR)′ data，and the hyperspectral analysis model of soil available phosphorus content prediction was established by partial least square regression (PLSR)，BP neural network and random forest. Coefficient of determination，root mean square error，relative analysis error and mean absolute error were used as evaluation indexes of the model. The results showed that the correlation between the original spectral characteristics and available phosphorus content was poor in each band，and the correlation between spectral reflectance and available phosphorus content could be improved by different spectral data transformation. Comparing the prediction accuracy of the models with different particle size treatments，the smaller the size of the sieve，the higher the estimation accuracy of the available phosphorus content. The optimal fitting models of the three methods were all over 0.25 mm sieve treatment. For the available phosphorus of 2.00，0.50 and 0.25 mm treatments，PLSR was used to establish the model with the most prominent prediction accuracy. The model determination coefficient，root mean square error，relative analysis error and average absolute error of the three methods were 0.76，10.96，2.07，8.69；0.77，12.52，2.00，9.74；0.77，10.90，2.13，8.81，respectively. Therefore，the characteristic bands selected by CARS algorithm and PLSR could better estimate the content of available phosphorus in soil of Urumqi.