Abstract: The study addressed the challenge that radio frequency identification technology, when applied for individual pig recognition in intensive breeding environments, was highly susceptible to multipath interference that degraded signal stability and reading accuracy. The objective was to develop a quantitative framework capable of characterizing time-varying channel disturbances and guiding system optimization. To achieve this, a dynamic interference evaluation model was established on the basis of the Rician fading channel theory, in which the power ratio between the direct path and the scattered components was continuously estimated rather than assumed to be constant. A dynamic estimation mechanism for the so-called K-factor was implemented through recursive optimization with sliding time windows, and the model was coupled with statistical features of the received signal strength indicator and phase fluctuations to capture the impact of environmental factors such as metallic structures and animal movement. A comprehensive interference scoring function was then formulated by combining the instantaneous K-factor and the variance of received signal strength, producing a continuous quantitative index scaled from 0 to 100 to represent interference intensity. The method was validated through both controlled laboratory simulations and on-site pig farm experiments. In the laboratory, a custom testbed was constructed with a high-precision spectrum analyzer, directive antennas, and resin pig models mounted on mobile platforms to reproduce dynamic occlusion and reflection. Measurements at 920 MHz demonstrated clear transitions from Rayleigh-like fading with severe envelope fluctuation under strong scattering to near-Gaussian stability under strong direct paths as the K-factor increased, confirming the suitability of the proposed channel representation. Field deployments in commercial pig houses further confirmed that different physical settings led to systematic differences: in background noise or simple tag-reader interaction scenarios, the interference score remained low, around forty-five to forty-nine, indicating relatively stable communication. In contrast, environments with stone walls produced scores near sixty, dynamic individual pig movement raised values to about sixty-five, dense static groups reached nearly seventy, and metal railings caused sharp degradation with scores around seventy-six. The most severe condition occurred when metallic structures coincided with pig groups, yielding scores approaching seventy-nine, which reflected substantial attenuation of the direct path and dominance of scattering. Correspondingly, average read success rates varied from ninety-eight percent in background conditions to only twenty-eight percent under metal railing interference, with received power levels ranging from approximately minus fifty-eight decibels-milliwatt in favorable conditions to minus seventy decibels-milliwatt in unfavorable cases. Comparative analysis against conventional modeling approaches highlighted the advantages of the proposed dynamic framework: the log-distance path loss model described only average attenuation and achieved about seventy-five percent read success, the Rayleigh model reached eighty percent but lacked adaptability to mixed propagation, and the static Rician model improved to eighty-five percent but could not capture temporal variability. Even the generalized Rician model, although effective in static industrial environments with about eighty-eight percent read success, was computationally heavy and not well suited for real-time agricultural settings. In contrast, the dynamic Rician approach consistently achieved ninety-two to ninety-four percent read rates, maintained higher received power, and obtained the best adaptability index of 0.91, proving its robustness under diverse and rapidly changing farm conditions. The findings demonstrated that incorporating a time-varying direct-to-scattered power ratio into the channel representation provided a realistic and flexible description of multipath propagation in livestock houses, accurately reflecting the layered contributions of structural reflection, animal density, and movement. The conclusion was that the dynamic interference evaluation model based on Rician channel theory offered a reliable quantitative tool for assessing signal stability, diagnosing high-risk interference zones, and guiding antenna placement and system configuration. It provided both theoretical support and practical data for deploying robust RFID systems in pig breeding environments, ultimately contributing to improved accuracy of individual animal monitoring and supporting the broader goal of intelligent livestock management.