Rubber particles and straw powder were used to prepare recycled rubber straw concrete, and the freeze–thaw test was conducted on the recycled rubber straw concrete using the quick-freezing method. The frost resistance of the recycled rubber straw concrete was evaluated by determining the relative dynamic modulus of elasticity, the rate of mass loss, and the flexural strength of the recycled rubber straw concrete in the process of freezing and thawing. SEM was used to observe the microstructure of the recycled rubber straw concrete after the freezing and thawing process. SEM observed the microstructure of recycled rubber straw concrete after freezing and thawing. The effect and mechanism of rubber admixture and straw admixture on the frost resistance of concrete were investigated by microanalysis. Based on the experimental data, the particle swarm algorithm and genetic algorithm were used to optimize the BP neural network to establish the prediction model of recycled rubber straw powder, and the results show that the PSO-BP neural network prediction model established in this paper has good accuracy and stability. It has a good prediction effect on the flexural strength and the number of freeze–thaw cycles of recycled rubber straw concrete under different mixing ratios.

Strength losses and poor compatibility between recycled crumb rubber (CR) and cement paste constrain the use of waste-tire polymers in concrete. This study investigated the combined use of corn straw ash (CSA) and KH570-treated crumb rubber (CR) through mechanical testing, microstructural characterization, and compressive strength prediction. Fifteen mixtures were examined, with compressive strength measured at 3, 7, and 28 d and flexural strength at 28 d. Among the investigated CSA replacement levels at 10% CR, 8% CSA produced the highest 28 d compressive and flexural strengths of 39.28 and 7.1 MPa, respectively. These values were 15.1% and 20.3% higher than those of the corresponding rubberized concrete without CSA, and 15.2% and 16.4% higher than those of the untreated-rubber mixture. The combined treatment therefore partially compensated for rubber-induced strength losses. X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy provided complementary evidence consistent with matrix refinement and improved rubber–paste interfacial compatibility. Backpropagation (BP), genetic-algorithm-optimized BP, and particle-swarm-optimized BP (PSO-BP) models were compared using 135 experimental records. PSO-BP yielded the lowest test-set root-mean-square error of 2.1713 MPa under the adopted data partition. These findings demonstrate the potential of combining agricultural ash incorporation with waste-rubber surface treatment to improve the strength performance of rubberized concrete and provide an experimental basis for composition selection.