基于深度数据挖掘的法律案例自动推荐方法

Automatic Recommendation Method for Legal Cases Based on Deep Data Mining

为改善推荐效果,研究基于深度数据挖掘的法律案例自动推荐方法。首先,建立法律案例深度推荐模型,通过计算权重和引入卷积神经网络捕捉空间关系,在特征和目标之间拟合特征关系。其次,利用注意力机制融合所有特征,使模型与查询文本匹配并使用全连接网络输出相关程度得分和分类预测。最后,对法律案例进行特征向量提取,将字符序列转化为语义表征向量序列,并聚合特征提取层的向量,以获取学习参数。实验结果表明,设计方法推荐案例的推荐效果较好。

To improve the effectiveness of recommendation,research on automatic recommendation methods for legal cases based on deep data mining.Establish a deep recommendation model for legal cases,capture the spatial relationship by calculating the weight and using Convolutional neural network,and fit the feature relationship between features and targets.Utilize attention mechanism to fuse all features,match the model with the query text,and use a fully connected network to output correlation score and classification prediction.Extract feature vectors from legal cases,convert character sequences into semantic representation vector sequences,and aggregate vectors from feature extraction layers to obtain learning parameters.Establish an evaluation system for legal case indicators,normalize the data and calculate the weights of feature attributes.Compare the weight values of the same legal case,and high weights indicate high recommendability to complete automatic recommendations.The experimental results show that the recommendation effect of the design method recommendation case is the best.