<p>Spiking neural P (SN P) systems are membrane computing models inspired by the structure and the information transmission and processing mechanisms of nerve neurons. The hierarchical spiking neural P systems with weights on multiple channels (HSNP-WMC systems) is proposed as a novel variant of SN P systems with a hierarchical structure. In the HSNP-WMC systems, the rules contained in the neurons in a layer have the same form. Neurons on a layer process the inputted information in parallel and transmit the processed results to the next layer for further processing. The synapses connecting neurons on two different layers have multiple channels and each channel is assigned a weight. Based on the HSNP-WMC system, a graph-based node classification algorithm is developed. Experiments on node classification using three citation network datasets are performed to evaluate the performance of the HSNP-WMC systems. Experimental results show that the HSNP-WMC systems have much better performance than the 13 baseline methods in classification accuracy, providing evidence for the effectiveness of the HSNP-WMC systems for graph-based node classification.</p>