As the source of the haze data, the reliability of the haze monitoring sites is very important to the reliability of the big data. Due to the lack of effective evaluation method for the haze monitoring points, the monitoring data is not reliable enough. In order to solve the problem that the perceived data was not reliable, a kind of perceptual source trust evaluation and selection model was proposed based on the data trigger detection method. When the perceived data arrived, the K-Means clustering algorithm and the statistical results were firstly used to calculate the benchmark data, then the trust degree of data was calculated by using the current perceived data, the benchmark data and the threshold values. Secondly, according to the location of the perceptual source, neighbor relationship was determined. The current perceived data and the data of the neighbors were compared, according to the absolute value of the difference and the value of the threshold, the neighbor recommendation trust degree was calculated. Finally, the comprehensive trust degree was calculated by using the truest degree of perceived data, the historical trust degree and the recommendation trust degree of the neighbor. The initial value of the historical trust was set as the number of monitoring items, and then updated by the comprehensive trust. Theoretical analysis and simulation results prove that the proposed method can effectively evaluate the perceived source, avoid the abnormal data, and reduce post processing overhead.