Accurate modeling of tire mechanical properties has emerged as a critical part of FSAE vehicle dynamics development. This simulation capability directly influences suspension design and cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure, and camber angle as input features, while the second uses tire temperature, ambient temperature, and ground temperature. The first network model simulates the magic formula model of the tire, and the second fine-tunes the lateral force, aligning moment, and overturning moment based on temperature characteristics. The grouped feature model exhibits high accuracy, generalization capability, and robustness. Meanwhile, it prevents secondary input features (such as temperature) from being completely dominated by primary input features (such as vertical load), facilitating the explanation of the influence of the two feature groups on tire characteristics. Based on the measured temperature values during the national competition, the neuron network model, co-simulated with VI-CarRealTime, rapidly calculated the required tire pressure value corresponding to the maximum lateral force, effectively freeing up pre-race practice time and achieving an outstanding ranking.