Categorization Algorithm of Vocal Music Online Course Resources Under the Background of MOOC Teaching

MOOC is an online course development model with modern education and teaching technology. The pervasive adoption of Massive Open Online Courses (MOOCs) in China has precipitated a notable influence on the transformation of vocal music pedagogy within local higher education institutions. MOOCs augment the reach of vocal music curricula, revolutionize conventional teaching methodologies in the discipline, acknowledge the ascendancy of students within the classroom milieu, and proficiently foster teacher-student interaction. In an attempt to scrutinize the ramifications of video contextual alterations on video categorization outcomes and devise strategies to enhance intra-class similarity and inter-class dispersion in video categorization, a multi-channel convolutional network video categorization methodology predicated on depth metric learning is formulated. The network architecture of multi-scale feature fusion is harnessed to architect a multi-channel convolutional video categorization network. In the metric learning framework, an interval allocation function grounded on negative samples to contextual range is propounded. This enables the network to learn intra-class similarity and inter-class dispersion. Therefore, this approach redirects the network’s attention toward recalcitrant samples. Evaluation learning and categorization tasks are executed concurrently during the training phase. The empirical findings corroborate that this methodology can appreciably elevate the precision of video categorization.