Spike sorting is an effective approach for analysis of neuron activities. With the increasing number of recording channels, online spike sorting is seen as a promising solution to relax the wireless transmission burden. Deep learning based methods provide superior spike sorting accuracy but require intensive computation offsetting the efficient transmission. To cut down the computation cost, a scattering convolution network (SCN) is proposed to extract features via wavelet scattering transform, then a lightweight convolutional neural network (CNN) is capable of sorting the spikes accurately. Experiment results demonstrate that the proposed SCN-CNN method achieves over 92% computation reduction and maintains a high classification accuracy of up to 99.97%, showing huge potential for online spike sorting on large-scale neural probes.