## 4 Conclusions  
This paper conducts an agricultural investment climate evaluation study in CEE countries based on an improved BP network. The main conclusions are as follows:  
First, this paper calculates the expected value of the agricultural investment environment in CEE countries by a preliminary screening of evaluation indicators through the correlation coefficient method and determining the weights of relevant indicators through the entropy method. Based on the BP neural network algorithm, the target error is finally achieved by training the sample and continuously debugging the parameters to reduce the target gap. The relative error between the test results and the expected results meets the target error requirement, which can effectively show that the constructed network model has good generalization ability and can effectively measure the agricultural investment environment in CEE countries. Second, applying BP neural network model to agricultural investment decision-making research field can avoid the influence of human factors, and the model has strong self-learning, self-organizing and adaptive capabilities. The results of the numerical examples also demonstrate the high accuracy of model training and the objective of evaluation results. The comprehensive evaluation framework for the agricultural investment climate constructed in this paper further improves the current evaluation index system. We attempt to propose an integrated model and apply it as a thorough appraisal technique to the assessment of agricultural investment climate. It can tackle the issues of subjective \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Ave.**} & \multirow{2}{*}{**Rank**} & **Relative Change** & \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Ave.**} & \multirow{2}{*}{**Rank**} & **Relative Change** \\  & & & **(2009/2018)** & & & & **(2009/2018)** \\ \hline Albania & 0.0369 & 16 & 122.4\% & Lithuania & 0.1556 & 4 & \(-\)14.3\% \\ Bosnia & 0.0549 & 14 & \(-\)23.7\% & Montenegro & 0.0646 & 12 & 31.8\% \\ Bulgaria & 0.0665 & 11 & \(-\)28.2\% & Macedonia & 0.0685 & 10 & \(-\)1.5\% \\ Croatia & 0.1412 & 6 & \(-\)14.3\% & Poland & 0.1063 & 9 & 13.0\% \\ Czech & 0.1607 & 2 & 2.7\% & Romania & 0.0520 & 15 & \(-\)39.3\% \\ Estonia & 0.1657 & 1 & 9.2\% & Serbia & 0.0554 & 13 & 6.0\% \\ Hungary & 0.1311 & 7 & \(-\)16.2\% & Slovakia & 0.1573 & 3 & \(-\)10.6\% \\ Latvia & 0.1296 & 8 & \(-\)13.5\% & Slovenia & 0.1544 & 5 & \(-\)21.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average evaluation results of technical climate in CEE countries. Figure 7: Evaluation results of technical climate in CEE countries from 2009 to 2018.  
one-sidedness, contrariness of assessment markers, and data exclusion common in conventional comprehensive assessment methods to improve the evaluation's precision fundamentally. Third, according to the evaluation result, the overall performance of the three Baltic countries, both in terms of the overall investment facilitation indicators and the subsystem indicators, is superior to that of the other regions to varying degrees, especially in the case of Estonia, which has a specific correlation with its socio-economic development. A higher economic development level usually means a more robust market system and a more open market system, directly reflecting a favorable investment climate. The Visegrad Group and Slovenia are among the best in CEE countries in terms of investment climate, with the Czech Republic scoring the highest in the region, ranking second in the CEE countries for many years in terms of overall indicators and the top three in terms of subsystem indicators, which is an excellent market climate. The Balkans, which is made up of emerging and developing countries, lags behind in terms of the overall level of investment facilitation, with all eight countries in the middle or lower range, due to the actual gap in social development.