Bone-conducted speech enhancement based on deep learning has reached a milestone recently. However， there are still some issues to prevent its real-world applications， such as large models and high computational complexities. In this paper， a lightweight deep learning model is proposed to improve the efficiency of bone-conducted speech enhancement. Inspired by the fact that convolution network has unique advantages in feature extraction with a few of parameters， convolution structures are introduced into the frequency dimensions of the spectrogram in our model. These structures can extract the details of the spectrogram in the time-frequency structures and explore the potential relationship between high and low frequency components. These new features extracted by CNN are fed into the improved long short-term memory network to recover high-frequency components information and reconstruct speech signals. From the experiments on bone conduction speech database， we can draw a conclusion that the proposed model can reconstruct the time-frequency details of the high-frequency components. While improving the enhancement performance， the model size and the computational complexity are reduced.