In this paper, we first study the implementation process of the AdaBoost model, initialize the weight distribution of training samples so that each sample weight is equal, then generate weak classifiers and calculate the minimum weighted error rate corresponding to each weak classifier, and then linearly combine the basic classifiers into strong classifiers by using weighted summation. Then the basic classifier weighting parameter calculation formula is improved by combining the characteristics of the logistics distribution function. Finally, the algorithm is used to analyze the rural governance willingness of river country residents, as well as to analyze the rural governance needs of rural residents at three levels: living conditions, public health and service facilities, to identify the types of rural governance, and to explore the rural governance patterns of different types of farm households. At the level of living conditions, farmers who wish to improve cooking fuel in 54 households, heating conditions in 61 households, bathing conditions in 40 households and family toilets in 53 households account for 43%, 49%, 32% and 42%, respectively. A total of 80 farming households, accounting for 29.41% of the research farming households, were optimized and upgraded, including 66 households in the key development area and 14 households in the production and living security area, accounting for 82.5% and 17.5% of the optimized and upgraded type, respectively. The research method of this paper has important references and reference significance for the construction of rural governance.