Teaching optimization algorithm is a new type of group intelligence algorithm, which simulates the teaching process of teachers, and this paper improves the algorithm to realize the improvement of music teachers’ teaching ability. Aiming at the shortcomings of the teaching optimization algorithm which is easy to mature prematurely, has low solution accuracy and converges to the local optimum, this paper proposes a teaching optimization algorithm which integrates the improved Tennessee whisker search. The algorithm combines Tent mapping and inverse learning strategy to initialize the population and improve the quality of the initial population. Tennessee whisker search is performed on teachers to improve their teaching ability. Incorporating the hybrid variation operator into the individual student variation formula allows the algorithm to quickly jump out of the local optimum dilemma. The experimental results show that the hybrid teaching optimization algorithm based on BASTLBO proposed in this paper has good solution accuracy and robustness in finding the optimum on different types of optimization problems. The algorithm in this paper can achieve better teaching ability results than the unimproved TLBO algorithm and the teaching optimization algorithm incorporating the hippocampus strategy, and the objective function on two different indexes is reduced by 8.75% and 7% compared with that of the TLBO algorithm, respectively, and the hybrid teaching multi-objective optimization model designed in this paper has stronger practicality.