In order to demonstrate whether the sparrow search algorithm can show good performance in optimization, this paper improves the prediction model by this algorithm and predicts the change data of wood mechanical properties under different conditions, which better reflects the connection between the process parameters of wood heat treatment and the change of wood mechanical properties. The article takes the five main mechanical property parameters of thermally modified wood: compressive strength along the grain, flexural strength, flexural elastic modulus, radial hardness, and tangential hardness, respectively, as the objects of study and improves the sparrow search algorithm by Tenting chaotic mapping and then optimizes the Back Propagation (BP) network model by this algorithm. The results show that the number of iterations of the optimized Tent-Sparrow search algorithm-Back Propagation network model (TSSA-BP) is only one-eighth that of the original BP network model, and the convergence speed is greatly improved, the root mean square error of the TSSA-BP model is at least one-half times that of the original BP model, and the optimized model fits the original data better in terms of predicted values; thus, this article provided a feasible prediction algorithm for the field related to the mechanical property changes of wood after heat treatment.

The support vector machine (SVM) model was applied to predict the color change of heat-modified wood after artificial weathering. In order to improve the prediction performance, the improved particle swarm optimization (IPSO) algorithm was used to optimize the parameters of the SVM model, and an improved particle swarm optimized support vector machine (IPSO-SVM) model was established on the basis of the nonlinear descending weight strategy to improve the particle swarm optimization. To verify the performance of the established model, the MAE, RMSE, and R² of the test set and training set were compared with the PSO-SVM model and the SVM model. Analysis of the results showed that compared to the PSO-SVM model and the SVM model, the IPSO-SVM model reduced the RMSE of the training set data by 49% and 72%, the MAE by 52% and 78%, the STD by 14% and 68%, the test set data by 6% and 24%, the MAE by 2% and 25%, and the STD by 22% and 29%, respectively. The results show that modeling studies using the IPSO-SVM model provide results showing that color changes in heat-modified wood after artificial weathering can be successfully predicted without expensive and time-consuming experimental studies.

Employing molecular dynamics software, three models—vacuum–cellulose, nitrogen–cellulose, and air–cellulose—were built to clarify, via a microscopic perspective, the macroscopic changes in single-chain cellulose undergoing vacuum, nitrogen, and air heat treatments. Kinetic simulations were run following model equilibrium within the NPT system of 423, 443, 463, 483, and 503 K. The energy variations, cell parameters, densities, mean square displacements, hydrogen bonding numbers, and mechanical parameters were analyzed for the three models. The findings demonstrate that as the temperature climbed, the cellular characteristics among two models—the nitrogen and vacuum models—decreased and subsequently increased. The nitrogen model reached its lowest value at 443 K. In contrast, the vacuum model reached its minimum value at 463 K. The vacuum heat treatment may enhance the structural stability of the single-chain cellulose more effectively than the nitrogen and air treatments because it increases the number of hydrogen bonds within the cellulose chain and stabilizes the mean square displacement. Furthermore, the temperature has an impact on the mechanical characteristics of the cellulose amorphous zone; the maximum values of E and G for the vacuum and nitrogen models are found at 463 and 443 K, respectively. The Young’s modulus and shear modulus were consistently more significant for the vacuum model at either temperature, and the Poisson’s ratio was the opposite. Therefore, the vacuum heat treatment can better maintain wood stiffness and deformation resistance, thus improving wood utilization. These findings provide an essential theoretical basis for wood processing and modification, which can help optimize the heat treatment and enhance wood’s utilization and added value.

Formaldehyde emission from cellulosic materials is an important consideration, especially for wood products, which are regulated by many countries in terms of legislation and may affect the health of users. In this study, molecular dynamics simulations were performed at different temperatures using two common wood-modification methods, heat treatment, and acetylation, and the diffusion coefficients of the models as well as the mechanical properties, were discussed. The results showed that the mean square displacement of the common heat treatment model was best at 493 K. The acetylated cellulose model at 483 K was able to achieve four times the diffusion coefficient of the common cellulose model, while the acetylated cellulose material would be weaker than the common heat-treated cellulose material in terms of mechanical properties. These findings provide some reference for formaldehyde pretreatment of wood products.