The traditional nonweighted least squares Support Vector Machine (SVM) and weighted least square SVM have a few disadvantages of processing low Signal-to-Noise Ratio (SNR) laser echo in the field of lidar bathymetry, a filtering method named HW-LS-SVM was proposed by combining robust least square and weighted least square SVM. Firstly, strong prior weight function, residual error and mean square error were calculated by elimination weight function, then the weight of least square SVM was computed by weight function. Finally, the echo signal was filtered by iterative computation. The simulation results show that HW-LS-SVM algorithm is more robust than least square SVM, Bayes least square SVM and the traditional weighted least square SVM. The results were satisfactory when the noise rate reached to 45%, and the correct rate of the extracted water surface and bottom was 100%. The extracted water depths from 4 groups of laser echoes in deep area and 4 groups in shallow area all agree with the background data. The proposed method has better anti-noise performance and is more suitable for the filtering processing of the low SNR lidar bathymetry signal.