Feasibility Research on Application of Artificial Intelligence Technology in Enterprises Based on Deep Learning

The industrial application of artificial intelligence technology is profoundly changing the competitive pattern of enterprises. However, small and medium-sized enterprises generally face bottlenecks such as high investment threshold, lack of talents and difficult scene adaptation in the process of technology landing. This study focuses on the feasibility discrimination problem of enterprise application of artificial intelligence technology, and proposes a deep learning framework based on LSTM-Transformer hybrid model. By integrating the three types of time series characteristics of enterprise basic information, business dynamics and technology application data, a multi-dimensional evaluation system is constructed. The model adopts layered architecture: LSTM layer captures short-term business fluctuations, Transformer layer mines long-term correlation between technical input and business effectiveness, and finally realizes two-classification of "feasible" and "temporarily infeasible" through Softmax. Based on the six-year data of 2006 enterprises, the mixed model accuracy reached 89.06% (±0.31%) and F1 score was 87.88% (±0.36%), which was significantly better than the single model and CNN-LSTM baseline. Confusion matrix shows that 93.7% of high recall guarantees the identification of potential enterprises, and 74.4% of specificity reveals negative class discrimination optimization space. This study provides a data-driven decision-making tool for SMEs to avoid technology investment risks.