Research on Prediction and Evaluation Model of Online Entrepreneurship Platforms Based on Deep Learning

This paper proposes a prediction and evaluation model of online entrepreneurship platforms based on deep learning, aiming to improve the reliability of evaluation through precise data analysis and simulation. The core algorithm of this model adopts variational autoencoder (VAE) to integrate multidimensional data features such as platform user behavior and market trends, establishing an effective evaluation framework. First, the model uses variational autoencoder to preprocess and reduce the dimension of data to capture potential nonlinear relationships to reduce noise interference; then, with the assistance of a deep neural network, the potential growth trend of the platform is modeled and predicted, which further improves the evaluation accuracy. The simulation employs data from actual online entrepreneurship platforms to carry out experiments and confirm the performance of the model. The results indicate that the model achieves high prediction accuracy for key evaluation indicators, with an error rate is reduced by approximately 12 % compared to traditional methods. Through sensitivity analysis of different parameters, the model shows high stability and adaptability in various scenarios.