Polymer additive manufacturing has emerged as a critical technology in aerospace, biomedical, and modern industrial applications. However, process parameter variations and environmental disturbances frequently induce defect formation during fabrication, including porosity and interlayer bonding deficiencies, consequently compromising material deposition homogeneity within printed components. To address this challenge, this study develops an optimized inverse identification framework for heterogeneous material parameters, incorporating structural feature-informed algorithm modifications specifically designed for 3D-printed architectures to enable defect identification. Numerical simulations demonstrate the method's robustness, maintaining reconstruction errors below 2.1% when processing displacement fields contaminated with 3% Gaussian noise. Experimental validation through full-field displacement measurements successfully reconstructed both equivalent and relative shear modulus distributions in printed specimens. The reconstructed equivalent shear modulus showed a 26.79% deviation from reference benchmarks, confirming the method's engineering applicability for mechanical property assessment of complex printed structures.