## 1 Introduction  
With the continuous development of science and technology, sci-tech journals, as carriers of academic content, have also developed rapidly, and their importance as links in the national innovation system has increased. The quality of sci-tech journals affects the development and progress of subjects; thus, the development of subjects requires the scientific and systematic evaluation of journals. The evaluation and analysis of scientific journals can not only improve the quality and influence of scientific journals but can also facilitate researchers in understanding current research priorities and hot spots. In sci-tech journal evaluation research, compared with qualitative evaluation [1] methods, which are more heavily influenced by subjective coloring, methods for calculating multiple metrics for quantitative evaluation based on the derivation of mathematical formulas have gradually become the mainstream direction of journal evaluation research. Quantitative evaluation methods initially focused on single metrics, with metrics such as the Price Index [2], the law of literature concentration [3], and the impact factor [4] being developed, which would then be combined with single metrics such as the H-index [5] and impact factor and assigned metric weights by principal component analysis [6] and the entropy weighting method [7] to obtain comprehensive evaluation metrics. At present, some studies focus on the scenario of \"data-based evaluation at the time\" and introduce artificial intelligence into journal evaluation, such as through the application of artificial intelligence to assign metric weights and for the effective selection of evaluation methodsto assist in journal evaluation tasks [8]. We focus on the task of \"comprehensive dynamic evaluation of sci-tech journals based on historical journal evaluation metrics\", build a new sci-tech journal evaluation metrics dataset, and try to analyze the possible future influence of sci-tech journals by comprehensively analyzing various metrics through using artificial intelligence in journal evaluation studies to guide the future development of sci-tech journals. In this study, we construct a new time series dataset with 18 journal evaluation metrics; propose applying machine learning and deep learning methods commonly used in the field of time series analysis to the task of sci-tech dynamic journal evaluation based on this dataset; construct models based on each of the nine mainstream methods in the field of time series analysis to conduct training and testing experiments on our multivariate short-time multi-series dataset; and provide evaluation metrics for these models. The final results confirm the generalizability of these methods for the comprehensive dynamic evaluation of sci-tech journals and show the LSTM model built on our dataset produces the best performance (MSE: 0.00037, MAE: 0.01238, accuracy based on 80% confidence: 72.442%). Based on the results of this study, a variety of practical application strategies can be extended, which can serve as a guide for rational resource allocation and for the subsequent development planning of sci-tech journals. **Contributions**:  
1. We propose a sci-tech journal evaluation task for the dynamic evaluation of scientific journals based on time series analysis. 2. Based on the collection and analysis of data from sci-tech journal publishing platforms, such as WanFang and ZhiWang, we construct a time series dataset of journal metrics, which is a multivariate short-time multi-series time series dataset. Our dataset provides data support and certain challenges to the field of multi-series time series data collaborative analysis. 3. We build models using nine mainstream time series data analysis methods of machine learning and deep learning to conduct various experiments on the task and dataset proposed in this study, and the results are compared and analyzed in detail. 4.