In order to broaden the application field of Deep Isolation Forest (DIF) algorithm. we combine the deep learning pre-training DenseNet-121 model and DIF algorithm, and proposes a DenseNet Deep Isolation Forest (DDIF) algorithm for exploring the effectiveness of the method on the industrial image anomaly detection dataset MVTec AD. However, the dimension of feature vector after feature extraction by DenseNet-121 model is quite high, and there may be the problem that some important feature attributes in the dataset cannot be selected when randomly selecting data attributes to construct the tree, so we also propose a Group Deep Isolation Forest (GDIF) algorithm and applies it to tabular datasets. Finally, based on the DDIF algorithm and combined with the GDIF algorithm, the DenseNet Group Deep Isolation Forest (DGDIF) algorithm is obtained, which solves the problem of missing important features in high-dimensional data. Different datasets were selected for anomaly detection, and it was found that the DDIF method outperforms other deep learning-based methods in 9 out of 15 image datasets; the GDIF method showed better AUROC values than other traditional classical anomaly detection algorithms in the 9 tabular datasets; and the DGDIF method outperforms the DGDIF method in 15 image datasets by 9 outperforms the DDIF method without referencing feature grouping. The experimental results validate the effectiveness of the proposed GDIF algorithm, DDIF algorithm and DGDIF algorithm.