Research on Classification Method of Civil Legal Liability Based on Machine Learning Algorithm

In judicial practice, the determination of civil legal liability is a crucial core link, the essence of which is to realize the reasonable attribution of responsibility for the consequences of acts in complex social relations through the deconstruction of legal elements and the analysis of the relevance of factual evidence. The interpretation space of legal concepts, the subjective tendency of evidence assessment, and the continuous emergence of emerging case types make the traditional manual adjudication mode face a double challenge in terms of efficiency and standard unity. In this context, machine learning technology provides a new path for liability determination through data-driven quantitative analysis methods. In this study, we optimize the parameters of the random forest model based on the sparrow search algorithm (SSA), construct the SSA-RF liability determination model, and conduct comparative experiments with traditional algorithms such as decision tree, support vector machine (SVM), BP neural network, and K nearest neighbor (KNN). The experimental results show that the SSA-RF model significantly leads in the core metrics of accuracy (0.98), recall (0.98), precision (1.00), and F1 score (0.99), demonstrating excellent classification stability and comprehensiveness. The study shows that integrated learning methods, especially the random forest model optimized by intelligent algorithms, have significant advantages in the correlation analysis of complex legal elements and factual evidence, while shallow models such as KNN are limited by algorithmic mechanisms that are difficult to adapt to such scenarios. The SSA-RF model proposed in this paper not only breaks through the predicament of difficult to quantify subjective discretion in traditional judicial practice, but also provides a reliable technical solution for building intelligent judicial assistance system, which is of great practical value for promoting the standardization of judicial decision-making and the improvement of trial quality and efficiency.