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The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.

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In the new stage of development, commercial banks actively play the role of high-quality financial services and contribute a vital force to revitalizing villages and the modernization of the Chinese style. This paper selects three dimensions of financial performance, serviceability for agriculture, rural areas, and farmers, and digital inclusion ability to construct an evaluation index system of commercial banks' service for rural revitalization, and uses the entropy weight TOPSIS model to measure its service level for rural regeneration. To simulate the evaluation process, this paper selects nine commercial banks as samples for measurement and data analysis. The study is helpful in comprehensively assessing the level of commercial banks' services for rural revitalization and provides the theoretical basis and data reference for bank managers, investors, and government departments.

Portfolio optimization is a popular method widely used in the financial industry. This paper analyzes the asset allocation analysis for pension and diversified assets in new energy vehicles, computer software, chip, information industry. There are four assets from different sectors. In this paper, Mean-variance and CAPM model were used for portfolio optimization. Meanwhile, the performance of the portfolio is analyzed in this paper by using weights. In addition we compare the final sharp ratio of accepting the Lump Sum or Defined Benefit. The result shows that in the CAPM model, when accepting Lump Sum, "AAPL" and "NVDA" have the maximum and the minimum weight. When selecting Defined Benefit, "ADBE" and "NVDA" have the maximum and minimum weights in the maximum ratio, respectively. This study may be useful for retirees to use in choosing whether to receive Lump Sum or Defined Benefit.