Optimization of talent training path of broadcasting and hosting art based on matrix model

This study explores how to optimize the path of talent cultivation in broadcasting and hosting arts. In this paper, the accuracy matrix is established according to the knowledge network graph theory. The accuracy matrix is estimated by the great likelihood function and solved using the Glasso algorithm to construct the accuracy matrix, network model. The matrix model is used to analyze the influencing factors of the career development of broadcast hosts and the relevance of the employment of broadcast host graduates to their majors. In the development path of the broadcasting hosts, the top three influencing factors are academic level, resilience, and expression ability, accounting for 20.69%, 18.77%, and 13.41%, respectively. Regarding professional relevance, only 5.88% of broadcast hosts have a relevance of 0.9 or above in their majors, while only 15.35% of broadcast graduates are engaged in industries with a relevance of 0.9 or above in their majors. The cultivation of talents in broadcasting and hosting art should focus on students’ professional skills and personalities, and always look at the world with a developing vision and seek a way out with an innovative attitude.