NeuroLegalNet-RC: A ResNet-Capsule Network for Legal Regularization of Mental Well-Being in IT Workplaces

Organizations together with governments have started to develop regulatory frameworks because workplace mental well-being increasingly matters in IT sectors. NeuroLegalNet-RC represents the introduction of a new ResNet-Capsule Network system which checks and controls mental well-being compliance among IT personnel. Workplace productivity stands as a critical factor influenced by mental health so organizations need automated tools to ensure compliance with mental well-being regulations. The proposed model applies deep features extraction through ResNet from legal documents and HR policies alongside Capsule Networks for hierarchical framework dependency detection. NeuroLegalNet-RC processed a broad dataset containing workplace policies from all regions together with mental health survey records and regulatory documents while achieving 97.08% classification success which surpassed BERT + LSTM results (88.4%) and yielded better outcomes than TF-IDF + SVM (85.1%). The model exhibited exceptional accuracy and precision together with recall at 97.6% and F-1 score of 97.0% as it processed the policy classification among compliant and partially compliant and non-compliant ratings. NeuroLegalNet-RC combines compliance classification with risk scoring abilities that deliver specific legal violation predictions for corporate HR and legal departments to take action. The study demonstrates how the model functions with different legal requirements across regions thus serving as a powerful resource for international IT organizations that want to maintain regulatory compliance within healthy work settings.