The resolution of phase-contrast optical coherence elastography （PC-OCE） is constrained by the bandwidth of the system's light source， leading to poor quality in tomographic strain imaging. This limitation significantly hinders the practical implementation and advancement of PC-OCE. This study introduced a data-driven super-resolution strain measurement approach to tackle the challenge of strain reconstruction under restricted phase resolution. Firstly， according to the principle of PC-OCE， a simulation measurement model was built to obtain the required data set， which solved the problem that was is difficult to obtain the ground truth in the real measurement process. Secondly， a deep neural network was used to learn the mapping relationship between low-resolution phase and high-resolution strain through a data-driven manner， realizing the super-resolution measurement of strain. Finally， numerical validation and compression deformation loading experiments were employed to validate the efficacy of the method introduced in this study. The experimental results demonstrate that the approach presented in this study can reconstruct the strain measurement outcomes across a wide bandwidth despite operating under a narrow bandwidth output. Furthermore， the signal-to-noise ratio is enhanced by 18.4 dB and 1.45 dB in comparison to the vector method and conventional deep neural network for strain calculation. The proposed method overcomes the bandwidth limitation of the system light source， enabling super-resolution strain measurement under low-resolution phase input conditions. This advancement enhances the potential applications of phase-contrast optical coherence elastography in characterizing mechanical performance， detecting early internal damage， and other related areas.