Since the low security of sensors, poor detection area and resource limitation in Wireless Sensor Network (WSN) cause outlier data collected by nodes, an algorithm of the outlier node detection in WSN based on graph signal processing was proposed. Firstly, according to the sensor position features, a K-Nearest Neighbors ( KNN) graph signal model was established. Secondly, the statistical test quantity was built based on the smoothness ratio of the graph signal before and after low-pass filtering. Finally, the judgement of the existence of outlier nodes was realized through the statistical test quantity and decision threshold. Experiments on the public temperature dataset and PM2.5 dataset demonstrate that compared with algorithm of outlier node detection based on graph frequency domain, the proposed algorithm has the detection rate increased by 7% under the condition of single outlier node and has the detection rate of 98% under the condition of multiple outlier nodes, and keep high detection rate under the condition of outlier node with small deviation value.