This article introduces a novel method that draws on big data thinking, treating the weather system as a comprehensive entity in which the interactions of the high, middle, and low-level atmospheres, as well as the influences of static, thermal, and dynamic conditions, are considered. It utilises a novel approach to comprehensive similarity assessment through situation field analysis, using derived data from numerical weather models and reanalysed grid data of various meteorological elements as its fundamental characteristics. The approach begins by employing the machine learning Principal Component Analysis (PCA) method to condense the features of the original grid field data, making it adaptable to the resource processing capabilities of conventional business platforms. Subsequently, the derived dimensional feature data of different meteorological elements at various spatial levels are normalised to ensure a balanced effect when participating in similarity calculations. The constructed sample-derived feature factor matrix, suitable for comprehensive weather similarity analysis, undergoes calculation of the similarity distance for each feature dimension among the samples. Based on the variance contribution rates of the initial field information contained in the data from different “principal component” dimensions, different weights are assigned to the similarity distance results of each dimension, yielding a comprehensive similarity distance. Finally, using the K-Nearest Neighbours (KNN) algorithm, the method provides the most comprehensive similar sequence in the historical weather situation database for the target sample, thus upgrading and improving traditional methods of similar weather forecasting. This method provides a multi-element and multi-level “stereoscopic” comprehensive similarity, aiding forecasters in better understanding the structure and evolution of weather systems and, consequently, more accurately assessing the possible occurrence of related weather phenomena. Comparative analysis and testing applications indicate that the results of comprehensive similarity analysis are superior to traditional “sliced” similarity analysis, which only targets single meteorological elements or altitude levels, particularly in terms of matching critical weather system positions and strength features. It resolves issues such as inconsistent results of similar weather situation analysis for different “slices” and poor forecast stability. This method provides more direct and efficient assistance in weather analysis and forecasting and holds promising prospects for refined meteorological forecasting services. In several instances of extreme precipitation meteorological forecasting services in the Guangxi region since 2023, this method achieves significant application effectiveness.