With the rapid development of intelligent manufacturing in the Pearl River Delta region, the demand for high-quality and composite talents in robotics engineering has become increasingly urgent. However, traditional educational training models suffer from deficiencies in personalization and dynamic adaptability. To address this challenge, a research project was conducted to design an educational path optimization and course recommendation system based on a hybrid genetic algorithm (HGA) and a collaborative filtering algorithm. Experimental results show that the HGA performs excellently in optimizing learning paths, achieving an optimal solution with a fitness of 0.95 within 100 generations when the mutation probability is set to 0.08. Meanwhile, the collaborative filtering algorithm achieves high precision and recall rates in the recommendation system, with precision ranging from 86.7% to 88.9% and recall rate remaining around 88.2% when the number of recommendations is between 15 and 18. Furthermore, the HGA outperforms the BP-CNN algorithm in terms of diversity and novelty, with diversity and novelty indices of 0.94 and 0.92, respectively, for 15 recommendations. In summary, through the optimization of algorithms, this system realizes personalized design and precise recommendation of educational paths, providing effective support for the cultivation of talents in robotics engineering and demonstrating the application potential of intelligent educational models.