A Data-Driven and Experience-Driven Iterative Design Method for Intelligent Products

To address the insufficient integration of data and experience-driven approaches in the iteration of intelligent products, this study designs an original DECI algorithm, constructs an integrated iterative design framework, and conducts experimental verification. The DECI algorithm achieves subjective experience quantification and dynamic dual-modal collaboration through three innovations: an experience intent embedding module, an adaptive dynamic weighting mechanism, and a closed-loop iterative update strategy, overcoming the limitations of static fusion. A four-layer progressive framework is built, covering data acquisition, feature processing, fusion iteration, and effect output layers, opening up the dual-modal fusion path and forming a closed-loop iterative chain throughout the entire process. Using intelligent office software and mobile service apps as examples, multi-dimensional experiments were conducted with three comparative algorithms. The results show that the DECI algorithm reduces response time and error rate by 10.1%-40.5% compared to the best comparative algorithm, increases user satisfaction by 4.8%-23.4%, shortens the iteration cycle by 16.1%-23.9%, and controls performance fluctuation in high-concurrency scenarios within 2.1%, with all differences being statistically significant. This study constructs an iterative scheme that balances quantitative accuracy and subjective experience, providing theoretical support and an engineering paradigm for the efficient iteration of intelligent products.