Local credit cooperatives have long played an important role in local financial services. It has made a significant contribution to agricultural production, farmers’ incomes, and the economic development of rural areas. In particular, as a financial instrument serving farmers, microfinance management by local credit cooperatives plays a key role in pursuing profits and fulfilling social responsibility. It was therefore important to obtain effective instruments for combating poverty in rural areas from all walks of society. This paper first outlines the development of microfinance loans in Germany and other countries and describes the current situation and some of the challenges facing local credit cooperatives in financial management. Next, we present the basic concepts of data mining, describe the common methods and key techniques of data mining, analyze and compare the properties of the individual data, and show how the associated mining can actually be performed. Next, we will explain the basic model of microfinance for farmers and some risks in detail and analyze and evaluate the characteristics of these risks in the context of local credit cooperatives. As a result, this paper proposes an improved deep convolutional neural network. The optimized algorithm selects the optimal weight threshold value and different iteration times. The results are fewer errors, the results are closer to the correct data, and the efficiency is better than before. The algorithm is more efficient because errors have been greatly reduced and the time spent on them has been slightly reduced.

中文翻译：

---

基于深度卷积神经网络的农村金融组织空间结构与服务管理模型构建

地方信用社长期以来在地方金融服务中发挥着重要作用。为农业生产、农民增收、农村经济发展做出了重大贡献。尤其是地方信用社小额信贷经营作为服务农民的金融工具，在追求利润、履行社会责任方面发挥着关键作用。因此，从社会各界获得消除农村贫困的有效工具非常重要。本文首先概述了德国和其他国家小额信贷的发展情况，并描述了当地信用社在财务管理方面的现状和面临的一些挑战。接下来，我们介绍数据挖掘的基本概念，描述数据挖掘的常用方法和关键技术，分析和比较各个数据的属性，并展示如何实际执行相关的挖掘。接下来，我们将详细讲解农户小额信贷的基本模式和一些风险，并在地方信用社的背景下分析和评估这些风险的特征。因此，本文提出了一种改进的深度卷积神经网络。优化算法选择最佳的权重阈值和不同的迭代次数。结果错误更少，结果更接近正确数据，效率比以前更好。该算法更加高效，因为错误已大大减少，并且花费在错误上的时间也略有减少。接下来，我们将详细讲解农户小额信贷的基本模式和一些风险，并在地方信用社的背景下分析和评估这些风险的特征。因此，本文提出了一种改进的深度卷积神经网络。优化算法选择最佳的权重阈值和不同的迭代次数。结果错误更少，结果更接近正确数据，效率比以前更好。该算法更加高效，因为错误已大大减少，并且花费在错误上的时间也略有减少。接下来，我们将详细讲解农户小额信贷的基本模式和一些风险，并在地方信用社的背景下分析和评估这些风险的特征。因此，本文提出了一种改进的深度卷积神经网络。优化算法选择最佳的权重阈值和不同的迭代次数。结果错误更少，结果更接近正确数据，效率比以前更好。该算法更加高效，因为错误已大大减少，并且花费在错误上的时间也略有减少。本文提出了一种改进的深度卷积神经网络。优化算法选择最佳的权重阈值和不同的迭代次数。结果错误更少，结果更接近正确数据，效率比以前更好。该算法更加高效，因为错误已大大减少，并且花费在错误上的时间也略有减少。本文提出了一种改进的深度卷积神经网络。优化算法选择最佳的权重阈值和不同的迭代次数。结果错误更少，结果更接近正确数据，效率比以前更好。该算法更加高效，因为错误已大大减少，并且花费在错误上的时间也略有减少。