The traffic data in optical communication networks has the characteristics of large-scale and high dimensionality, and the inconsistency of data dimensions amplifies the differences between the data, resulting in unsatisfactory interpolation effects. Therefore, a continuous interpolation method for optical communication network traffic data based on improved transfer learning is proposed. The Box-Cox transformation method is used to standardize the traffic data and unify the data scales and dimensions. The convolutional neural network is improved using deep learning theory and VNet technology. By updating the network parameters, the continuous interpolation results are matched with the ideal data, obtaining the continuous interpolation results of the traffic data. Experimental results show that the signal-to-noise ratio of the proposed method is always higher than 27.83 dB, and the frequency-waveform distribution graph is most similar to the ideal data, with a coefficient of determination above 0.8, which can obtain high-quality interpolation results.