In recent years, the risk accumulation problem in the US credit bond market has become increasingly prominent against the backdrop of economic cycle fluctuations and external shocks such as epidemics and geopolitical events. Therefore, based on the LSSVM model and combined with the nonlinear and time-varying characteristics of the credit bond market, this study optimized the risk detection method and analyzed the performance of the model through empirical research. This article first provides an overview of the definition and related theories of credit risk. Then, a classification model for credit risk detection is constructed using least squares support vector machine (LSSVM), and a constraint optimization function is set. Finally, this experiment collects sample data from the US bond market for data cleaning, processing, and training. The data analysis results indicate that there is a certain correlation between the actual values and the predicted values. The support vector regression (SVR) model has a good fitting effect on the data. The area under the curve (AUC) value is 0.91, indicating that the model has high discriminative ability. This model not only improves the risk prediction ability, but also shows significant advantages in data complexity and calculation efficiency, providing a new technical idea for risk prevention and control of bond market.