Surgical robots can avoid damage to important tissues through preoperative path planning before surgery. As they have high operational accuracy， they are widely used in needle biopsy operations. However， surgical robots cannot currently achieve accurate and real-time acquisition of puncture needle shape information. This problem makes it difficult for them to autonomously avoid obstacles and prevents the needle from precisely reaching its target in order to carry the puncture procedure out. Therefore， studying a shape perception method for needles to provide shape information feedback for autonomous/master-slave punctures by surgical robots， is important for improving the safety and accuracy of puncture surgery. Therefore， a shape self-sensing puncture needle that integrates distributed optical fiber sensing technology and artificial intelligence was developed in this study. A neural network model algorithm was trained using training data comprising the center wavelength data and shape data of the fiber Bragg grating under different bending states， both of which were obtained from a static calibration experiment. Then， the model was used to realize the three-dimensional shape reconstruction of the puncture needle. Experimental results indicate that the maximum error of the needle shape reconstruction is 0.90 mm， and that the maximum error of the bending directional angle is 5.03°. As regards the dynamic experiment， the maximum error of the shape reconstruction is 0.84 mm， and the maximum error of the bending directional angle is 1.02°； the reliability of the model is thus validated. Thus， the proposed shape self-sensing puncture needle can accurately realize real-time acquisition of the shape， and it has broad application prospects in needle shape perception and regulation during autonomous puncture and master-slave operations by surgical robots.