A model for assessing college students’ entrepreneurial abilities based on deep learning and big data

Abstract Entrepreneurship is widely acknowledged as a catalyst for innovation and economic growth; assessing entrepreneurial abilities in college students is a strategic imperative in higher education. Conventional evaluation approaches often suffer from limited scalability, subjectivity, and insufficient predictive power, particularly when applied to large and complex datasets. These challenges necessitate advanced, intelligent frameworks capable of extracting meaningful patterns from educational data. This research proposes a novel deep learning architecture named Battle Royale Optimized Self-Attention Bi-Directional Long Short-Term Memory (BRO-SA-Bi-LSTM) to assess entrepreneurial ability among college students by leveraging big data. The BRO algorithm enhances exploration and convergence during training, while the SA mechanism enables the model to assign dynamic importance to key features. The Bi-LSTM structure captures both past and future contextual dependencies, improving temporal learning and feature retention. The dataset, compiled from multiple academic institutions, includes variables such as academic performance in entrepreneurship courses, participation in innovation clubs, entrepreneurial experience, structured training involvement, and funding acquisition. Z-score normalization is applied during preprocessing to standardize data distributions. PCA is utilized for dimensionality reduction and feature extraction. The BRO-SA-Bi-LSTM model is implemented using Python and TensorFlow. Evaluation metrics include F1-score, accuracy, recall, and precision, ranging from 95 to 99%. Results show that the proposed model outperforms conventional architectures, delivering high predictive accuracy and superior generalization across test data. The proposed framework provides a scalable AI-based assessment strategy that can support curriculum design, personalized training, and large-scale educational evaluation.