# Innovative Technology Method Based on Evolutionary Game Model of Enterprise Sustainable Development and CNN–GRU

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## Abstract

**:**

## 1. Introduction

## 2. Related Research

## 3. DL Model

#### 3.1. Overall Structure

#### 3.2. EG Theory

#### 3.2.1. Basic Concepts of EG

#### 3.2.2. Characteristics and Classification of EG

#### 3.2.3. Differences from Classical Game Modeling

#### 3.3. Enterprise Game Pay-off Matrix

#### 3.4. Evolution Model of Enterprise Growth Network PO

#### 3.5. Model Solution and Stability Analysis

#### 3.6. DL Network Model Based on CNN–GRU

#### 3.7. CNN

#### 3.8. GRU

## 4. Experiments and Analysis

#### 4.1. Experimental Environment Configuration

#### 4.2. Datasets

#### 4.3. Evaluation Indicators

#### 4.4. Model Training

#### 4.5. Comparative Analysis

#### 4.6. Application of the Game Pay-off Matrix Model

#### 4.7. Comparison of Evolution Curves

## 5. Conclusions

- Focus on how to accurately predict the growth of enterprises considering the complex relationship structure in social enterprises, so as to have a precise positioning for the growth of enterprises.
- Pay attention to the change in enterprise relationship structure over time, and promote the sustainable development of enterprises from the applicability and improvement strategy of the enterprise growth innovation method.
- Focus on the complex relationship structure in the enterprise and the trend in emotional public opinion of enterprise personnel in a specific environment, so as to accurately predict the development of enterprises.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Enterprise | |||
---|---|---|---|

${\mathit{l}}_{1}$ | ${\mathit{l}}_{2}$ | ||

User | ${k}_{1}$ | $-{H}_{3}+{W}_{1}\left(1+a\right)+{P}_{1}$ $-{W}_{2}-{Q}_{2}$ | $-{H}_{2}+{W}_{1}\left(1-b\right)+{P}_{1}-{Q}_{1}$ $-{H}_{4}+{W}_{2}+{P}_{2}+{Q}_{1}$ |

${k}_{2}$ | $-{H}_{2}+{W}_{1}\left(1-b\right)+{P}_{1}-{Q}_{1}$ $-{H}_{4}+{W}_{2}+{P}_{2}+{Q}_{1}$ | $-{H}_{2}+{W}_{1}\left(1-b\right)$ ${W}_{2}-{Q}_{2}$ |

Name | Configuration |
---|---|

OS | Windows 7 64 bit |

CPU | Intel (R) Core (TM) i7-6700 |

Memory | 16 GB |

Programming language | Python 3.5.4 |

DL framework | Keras 2.2.4 |

Data Distribution | Training Set | Validation Set | Test Set |
---|---|---|---|

Positive | 18675 | 3265 | 3358 |

Negative | 17943 | 2571 | 2864 |

Total | 36618 | 5836 | 6222 |

Estimate | |||
---|---|---|---|

0 | 1 | ||

True | 0 | TP | FN |

1 | FP | TN |

Method | A/% | P/% | R/% | F1/% |
---|---|---|---|---|

Proposed method | 92.21 | 89.33 | 91.86 | 91.64 |

Ref. [19] | 85.45 | 83.62 | 86.31 | 85.66 |

Ref. [20] | 84.52 | 81.35 | 82.05 | 82.97 |

Ref. [23] | 80.45 | 80.62 | 80.81 | 80.52 |

Combination | Span | Combination | Span |
---|---|---|---|

$\eta =0.1,\lambda =0.1$ | 10% | $\eta =0.1,\lambda =0.12$ | 30% |

$\eta =0.125,\lambda =0.1$ | 15% | $\eta =0.1,\lambda =0.14$ | 70% |

$\eta =0.15,\lambda =0.1$ | 30% | $\eta =0.1,\lambda =0.16$ | 85% |

$\eta =0.175,\lambda =0.1$ | 50% | $\eta =0.1,\lambda =0.18$ | 98% |

$\eta =0.2,\lambda =0.1$ | 70% | $\eta =0.1,\lambda =0.2$ | 100% |

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## Share and Cite

**MDPI and ACS Style**

Zhang, H.; Xu, X.
Innovative Technology Method Based on Evolutionary Game Model of Enterprise Sustainable Development and CNN–GRU. *Sustainability* **2023**, *15*, 4058.
https://doi.org/10.3390/su15054058

**AMA Style**

Zhang H, Xu X.
Innovative Technology Method Based on Evolutionary Game Model of Enterprise Sustainable Development and CNN–GRU. *Sustainability*. 2023; 15(5):4058.
https://doi.org/10.3390/su15054058

**Chicago/Turabian Style**

Zhang, Hongni, and Xiangyi Xu.
2023. "Innovative Technology Method Based on Evolutionary Game Model of Enterprise Sustainable Development and CNN–GRU" *Sustainability* 15, no. 5: 4058.
https://doi.org/10.3390/su15054058