Due to the lack of a scientific and effective evaluation mechanism for the selection of ice cover observation points and the deployment of observation equipment, it is not possible to fully evaluate the rationality and effectiveness of existing deployment points, nor to provide reliable guidance for future planned deployment points. This article mainly uses the DEM model and historical ice cover observation data of Guizhou Province, and uses GIS technology, principal component analysis algorithm, and k-means clustering algorithm to establish an effective evaluation model for ice cover observation deployment points that integrates terrain and meteorological conditions for 50 ice cover observation points. The study shows that through principal component analysis, the cumulative contribution rate of the first four principal components reaches 88.2877%, and the contributions of elevation, windward slope, temperature, and slope are the highest, And provide a fusion model of “micro terrain + meteorological conditions”. Based on the results of the fusion model, the overall effect of micro terrain and micro meteorology shows a normal distribution; Construct a two-dimensional matrix of “micro terrain + meteorological conditions” and ice cover observation data, and classify the two-dimensional matrix into 5 categories based on the sum of squared errors evaluation index; Based on the classification results, provide 5 priority evaluations for the rationality of icing observation points, and it is recommended not to deploy relevant observation equipment for the 4th and 5th priority points. This study can provide scientific guidance for the selection of deployment locations for artificial ice observation and online monitoring equipment, and can effectively evaluate the necessity and rationality of deployment locations.