Aiming at the problems of high complexity and computational intensity in optical signal-to-noise ratio (OSNR) estimation, a high-order quadrature amplitude modulation (QAM) signal OSNR estimation method based on lightweight random forest (RF) algorithm is proposed. This method maps high-order QAM signals with different OSNRs into different constellation diagram datasets, and uses these datasets to train the RF model, thereby achieving rapid OSNR estimation The simulation results show that when the real value of system OSNR is between 5~30 dB, the accuracy of OSNR estimation for 64QAM and 128QAM signals based on lightweight RF algorithm is close to 100%, the mean absolute error(MAE) of OSNR estimation for 64QAM signals is 0.08 dB, and the MAE for 128QAM is 0.12 dB, which is more accurate than the signal OSNR estimation results based on long short-term memory(LSTM) algorithm.