(1) Background: The present study aimed to investigate the association between body mass index (BMI) and muscular fitness in Chinese college freshmen. (2) Methods: A total of 6425 college freshmen in mainland China were recruited. BMI was classified as underweight (<18.5 kg/m2), normal weight (18.5~23.9 kg/m2), overweight (24~27.9 kg/m2), and obese (≥28 kg/m2), according to the Working Group on Obesity in China. Health-related physical fitness components including cardiorespiratory fitness, lower body explosive power, upper body muscular endurance, abdominal muscular endurance, flexibility, and vital capacity were assessed. Physical fitness index and muscular fitness index were calculated, respectively, as the sum score of the standardized values (z-score) of the corresponding components. Three regression models were used to evaluate the potential associations: a linear regression model, a polynomial regression model, and a restricted cubic spline regression model. Adjust R square was used to compare among models. (3) Results: Significant differences were observed among different BMI categories in nearly all physical fitness components as well as physical fitness z-score and muscular fitness z-score (p < 0.001), regardless of gender. Significant linear associations were found between BMI and physical fitness z-score as well as muscular fitness z-score among total, male, and female groups (p < 0.05). However, the restricted cubic spline regression model showed a better fitting effect (adjust R2 was 7.9%, 11.2%, and 4.8% in total, male, and female for physical fitness and 7.7%, 15.7%, and 4.0%, for muscular fitness, respectively), compared with the linear and polynomial regression models, presented by a higher adjusted R2. Restricted cubic splines analysis showed that BMI value and physical fitness z-score showed a non-linear relationship with an approximate inverted U curve in all groups, while an approximate reversed J-shaped association was observed between BMI and muscular fitness z-score in all groups. (4) Conclusions: The present study showed a nonlinear negative relationship between BMI and physical fitness with underweight and overweight/obese college freshmen having poorer physical fitness and muscular fitness than their normal BMI peers, which may provide useful evidence to the development of public health recommendations and encourage the health management of young adults. Future studies should further explore the relationship between BMI and muscular fitness with multi-centered large sample size studies.

The objective is to address the issue of simplification of physical education classes offered by large colleges and universities. The evaluation standard of physical education curriculum is not unified. The physical education management system focuses on the functions of collecting information, sorting, and statistics and has low timeliness and guiding significance. This paper puts forward an analysis of the construction principle of physical fitness training target system based on machine learning and data mining. This paper uses informational analysis to statistically analyze the healthy behavior of college students, to guide the physical education of college students, and to propose a model for the analysis of healthy sports behavior of college students based on data mining technology. Create a decision tree template for students whose cardiovascular function does not meet the standard using the decision tree algorithm. The association rule algorithm is used to mine the association of five indexes of physical health, so as to judge the hidden law between students’ physical fitness and behavior habits and get the correlation information of various physical health indicators. The simulation results show that, through the prediction of college students’ healthy sports behavior data, when the sample point is 5, the original value data is 16, which is higher than the estimated value, the convergence of the overall data characteristic distribution is good, and the disturbance error is low. Therefore, using this method to analyze the application of college students’ healthy sports behavior has a high accuracy of sports-related data mining and can effectively guide college students’ sports management and training.