For decision-making on the attributes and time weights existing in a dynamic intuitionistic fuzzy environment, a new ATS-generalized weighted intuitionistic fuzzy Bonferroni mean operator MADM model based on dynamic comprehensive time entropy and an ATS-generalized weighted intuitionistic fuzzy Bonferroni mean operator was established by taking into consideration the intrinsic correlations between attributes. An intuitionistic fuzzy decision matrix with the same time sequence was integrated into the model. According to the idea of “laying more stress on the present than on the past,” a time sequence weight considering both the subjective preferences and the objective information of samples was obtained to overcome the irrationality of subjective value assignment on existing time sequence weight and ideal time weighting. Based on dynamic comprehensive time entropy, the model not only reflects the degree of importance attached to the latest data but also gives consideration to the subjective preferences of decision-makers in order to set a new vector for time sequence weight. The dynamic intuitionistic fuzzy weighted operator was used to conduct aggregation to obtain a dynamic intuitionistic fuzzy comprehensive value, and the obtained results are sorted by the sorting function of intuitionistic fuzzy sets. The best alternative was selected and applied to a case study on green building project selection. The results indicate that the proposed method is comprehensive, scientific, and feasible.

Image recognition is the key to smart logistics systems. Traditional handwriting feature extraction is difficult to meet the requirements of image recognition. Deep learning is used for image recognition. Firstly, convolutional neural network (CNN) and deep Boltzmann machines under deep learning are introduced. Second, cellular neural networks are used to perform feature recognition and extraction on images. Finally, a Parzen classifier is used to classify the obtained image features. The novelty is that through the structural design and research of the intelligent logistics system, the CNN is combined to construct a management system of supply chain logistics of image recognition and information processing. The experimental results show that the recognition accuracy time of the proposed improved fusion algorithm on the Mixed National Institute of Standards and Technology data set is 198.85 s. When the improved algorithm achieves the same recognition accuracy, it takes 159.65 s. The recognition efficiency of the improved algorithm is 19.71% higher than that of the unimproved algorithm. In addition, when the unimproved algorithm reaches the maximum number of iterations, the error rate is 2.47%. The error rate of the improved algorithm is only 0.74%. This study provides a basis for improving the image recognition accuracy and has certain practical value.

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