This study investigates the application of deep learning algorithms to enhance the recognition and development of innovative abilities in entrepreneurial talent cultivation. In today’s rapidly evolving economic landscape, fostering entrepreneurship and promoting creativity are critical for sustainable growth. This research proposes an innovative approach that leverages the power of deep learning algorithms to accurately identify and foster innovative abilities within the realm of entrepreneurship education. This work proposes a one-dimensional lightweight convolutional neural network-based method called 1D-LCNN for analyzing the innovation ability in the cultivation of entrepreneurship and innovation. Adding an 1 × 1 convolution kernel, as this approach does, improves the model's capacity for nonlinear expression. Overfitting is avoided and model parameters are decreased by using a global average pooling layer in place of the conventional FC layer in a convolutional neural network. The experimental findings demonstrate the effectiveness of the proposed strategy. The findings demonstrate promising results, indicating the potential of deep learning algorithms in identifying and nurturing essential innovative abilities in entrepreneurial talent development. This research provides valuable insights and practical implications for entrepreneurship education and talent cultivation.