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Optical coherence elastography (OCE), leveraging high\u2010resolution optical coherence tomography, promises tissue stiffness assessment. Traditional OCE relies on data processing of the time\u2010of\u2010flight method and encounters challenges like low repeatability. Our study presents an optimized data processing workflow integrating OCE with deep learning to predict ocular tissue biomechanical properties. The concentration prediction network (CPN), a 3D convolutional neural network, predicts sample's concentrations and calculates the Young's modulus based on the relationship between agar concentration and Young's modulus from mechanical testing. The CPN showed high accuracy, with a mean absolute error of 0.028\u2009\u00b1\u20090.036 for training and 0.036\u2009\u00b1\u20090.024 for testing data of agar phantoms. In\u00a0situ porcine corneas with various intraocular pressures was measured, yielding corneal biomechanical distribution via deep learning method. 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