Machine learning methods can be well combined with software testing to enhance test effect, but few scholars have applied it to test data generation. In order to further improve the efficiency of test data generation, a chained model combining support vector machine (SVM) and extreme gradient boosting (XGBoost) is proposed, and multi-path test data generation is realized by a genetic algorithm based on the chained model. Firstly, this study uses certain samples to train several sub-models (i.e., SVM and XGBoost) for predicting the state of path nodes, filters the optimal sub-models based on the prediction accuracy value of the sub-models, and links the optimal sub-models in sequence according to the order of the path nodes, so as to form a chained model, namely chained SVM and XGBoost (C-SVMXGBoost). When using the genetic algorithm to generate test cases, the study makes use of the chained model that is trained instead of the instrumentation method to obtain the test data coverage path (i.e., predicted path), finds the path set with the predicted path similar to the target path, performs instrumentation verification on the predicted path with similar path sets, obtains accurate paths, and calculates fitness values. In the crossover and mutation process, excellent test cases with a large path level depth in the sample set are introduced for reuse to generate test data covering the target path. Finally, individuals with higher fitness during the evolutionary generation are saved, and C-SVMXGBoost is updated, so as to further improve the test efficiency. Experiments show that C-SVMXGBoost is more suitable for solving the path prediction problem and improving the test efficiency than other chained models. Moreover, compared with the existing classical methods, the proposed method can increase the coverage rate by up to 15%. The mean evolutionary algebra is also reduced, and the reduction percentage can reach 65% on programs of large size.