In the context of networked teaching and talent cultivation, information deep integrated learning mining is applied to the field of education. However, it is found that the application of deep integrated learning mining algorithms in the digital trade talent cultivation model is not satisfactory. Aiming at the defects and deficiencies of the current algorithms, a deep integrated learning mining algorithm is proposed for the labeled demand information of digital trade talent cultivation mode. Sniffing technology is utilized to gather labeled demand data concerning talent training modes. This data is then deeply integrated to create demand labels for said modes. Neural network algorithms are then employed to learn and extract insights from this labeled demand data, facilitating comprehensive integration and extraction. Experimental results demonstrate that the algorithm achieves a recall rate surpassing 90% and a normalized score exceeding 0.8, showcasing its capability for accurate extraction of demand information.