To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid–continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the Particle Swarm Optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master–slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability.

Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering approaches struggle with cold-start items owing to dependency on historical interactions, and (2) LLMs lack inherent capability to model interactive graph directly. To bridge this gap, we propose a dual-enhancement method that integrates interactive graph learning and LLM-based semantic understanding for product bundling. Our method introduces a graph-to-text paradigm, which leverages a Dynamic Concept Binding Mechanism (DCBM) to translate graph structures into natural language prompts. The DCBM plays a critical role in aligning domain-specific entities with LLM tokenization, enabling effective comprehension of combinatorial constraints. Experiments on three benchmarks (POG, POG_dense, Steam) demonstrate 6.3–26.5% relative improvements in HitRate@1 over the strongest baseline.