Amidst profound transformations witnessed in the contemporary occupational landscape, precipitated by persistent forces of globalization and digitalization, an urgent emergence of the need for a comprehensive array of adaptive skills has been observed. Addressing this imperative, the merging of vocational education and lifelong learning is deemed essential, emerging as a cornerstone in contemporary educational discourse. Though the import of adaptive skills is universally acknowledged, the means to impart such training effectively remains elusive. Present technological strategies reveal shortcomings in provisioning individualized learning experiences and in extracting semantic information from textual content. Through the current investigation, a strategy is introduced whereby adaptive skills are fortified via intelligent recommendations, in tandem with an optimized Long-Short Term Memory (LSTM) network structure, aiming for an incisive classification of adaptive skill training texts. Such strategies not only delineate a fresh theoretical paradigm for adaptive skill instruction but also proffer cutting-edge technical reinforcement for the intertwined realms of vocational education and lifelong learning. The innovation of this research lies in the proposal of an adaptive skill enhancement method based on intelligent recommendation and the design of an intelligent test item recommendation algorithm for adaptive skill enhancement. Additionally, the research introduces innovative improvements to the LSTM network structure by incorporating thematic semantic information, enabling the model to generate more accurate text representations, thereby significantly improving classification performance and the accuracy of personalized recommendations. These contributions provide effective technical support for enhancing the effectiveness of adaptive skill training.