diff --git a/tools/analysis_tools/confusion_matrix.py b/tools/analysis_tools/confusion_matrix.py
index 9a87bc14c..39756cdfd 100644
--- a/tools/analysis_tools/confusion_matrix.py
+++ b/tools/analysis_tools/confusion_matrix.py
@@ -5,10 +5,14 @@ import os
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.ticker import MultipleLocator
-from mmengine import Config, DictAction
-from mmengine.utils import ProgressBar, load
+from mmengine.config import Config, DictAction
+from mmengine.registry import init_default_scope
+from mmengine.utils import mkdir_or_exist, progressbar
+from PIL import Image
 
-from mmseg.datasets import build_dataset
+from mmseg.registry import DATASETS
+
+init_default_scope('mmseg')
 
 
 def parse_args():
@@ -16,7 +20,7 @@ def parse_args():
         description='Generate confusion matrix from segmentation results')
     parser.add_argument('config', help='test config file path')
     parser.add_argument(
-        'prediction_path', help='prediction path where test .pkl result')
+        'prediction_path', help='prediction path where test folder result')
     parser.add_argument(
         'save_dir', help='directory where confusion matrix will be saved')
     parser.add_argument(
@@ -50,15 +54,23 @@ def calculate_confusion_matrix(dataset, results):
         dataset (Dataset): Test or val dataset.
         results (list[ndarray]): A list of segmentation results in each image.
     """
-    n = len(dataset.CLASSES)
+    n = len(dataset.METAINFO['classes'])
     confusion_matrix = np.zeros(shape=[n, n])
     assert len(dataset) == len(results)
-    prog_bar = ProgressBar(len(results))
+    ignore_index = dataset.ignore_index
+    reduce_zero_label = dataset.reduce_zero_label
+    prog_bar = progressbar.ProgressBar(len(results))
     for idx, per_img_res in enumerate(results):
         res_segm = per_img_res
-        gt_segm = dataset.get_gt_seg_map_by_idx(idx)
+        gt_segm = dataset[idx]['data_samples'] \
+            .gt_sem_seg.data.squeeze().numpy().astype(np.uint8)
+        gt_segm, res_segm = gt_segm.flatten(), res_segm.flatten()
+        if reduce_zero_label:
+            gt_segm = gt_segm - 1
+        to_ignore = gt_segm == ignore_index
+
+        gt_segm, res_segm = gt_segm[~to_ignore], res_segm[~to_ignore]
         inds = n * gt_segm + res_segm
-        inds = inds.flatten()
         mat = np.bincount(inds, minlength=n**2).reshape(n, n)
         confusion_matrix += mat
         prog_bar.update()
@@ -70,7 +82,7 @@ def plot_confusion_matrix(confusion_matrix,
                           save_dir=None,
                           show=True,
                           title='Normalized Confusion Matrix',
-                          color_theme='winter'):
+                          color_theme='OrRd'):
     """Draw confusion matrix with matplotlib.
 
     Args:
@@ -89,14 +101,15 @@ def plot_confusion_matrix(confusion_matrix,
 
     num_classes = len(labels)
     fig, ax = plt.subplots(
-        figsize=(2 * num_classes, 2 * num_classes * 0.8), dpi=180)
+        figsize=(2 * num_classes, 2 * num_classes * 0.8), dpi=300)
     cmap = plt.get_cmap(color_theme)
     im = ax.imshow(confusion_matrix, cmap=cmap)
-    plt.colorbar(mappable=im, ax=ax)
+    colorbar = plt.colorbar(mappable=im, ax=ax)
+    colorbar.ax.tick_params(labelsize=20)  # 设置 colorbar 标签的字体大小
 
-    title_font = {'weight': 'bold', 'size': 12}
+    title_font = {'weight': 'bold', 'size': 20}
     ax.set_title(title, fontdict=title_font)
-    label_font = {'size': 10}
+    label_font = {'size': 40}
     plt.ylabel('Ground Truth Label', fontdict=label_font)
     plt.xlabel('Prediction Label', fontdict=label_font)
 
@@ -116,8 +129,8 @@ def plot_confusion_matrix(confusion_matrix,
     # draw label
     ax.set_xticks(np.arange(num_classes))
     ax.set_yticks(np.arange(num_classes))
-    ax.set_xticklabels(labels)
-    ax.set_yticklabels(labels)
+    ax.set_xticklabels(labels, fontsize=20)
+    ax.set_yticklabels(labels, fontsize=20)
 
     ax.tick_params(
         axis='x', bottom=False, top=True, labelbottom=False, labeltop=True)
@@ -135,13 +148,14 @@ def plot_confusion_matrix(confusion_matrix,
                           ) if not np.isnan(confusion_matrix[i, j]) else -1),
                 ha='center',
                 va='center',
-                color='w',
-                size=7)
+                color='k',
+                size=20)
 
     ax.set_ylim(len(confusion_matrix) - 0.5, -0.5)  # matplotlib>3.1.1
 
     fig.tight_layout()
     if save_dir is not None:
+        mkdir_or_exist(save_dir)
         plt.savefig(
             os.path.join(save_dir, 'confusion_matrix.png'), format='png')
     if show:
@@ -155,7 +169,12 @@ def main():
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
-    results = load(args.prediction_path)
+    results = []
+    for img in sorted(os.listdir(args.prediction_path)):
+        img = os.path.join(args.prediction_path, img)
+        image = Image.open(img)
+        image = np.copy(image)
+        results.append(image)
 
     assert isinstance(results, list)
     if isinstance(results[0], np.ndarray):
@@ -163,17 +182,11 @@ def main():
     else:
         raise TypeError('invalid type of prediction results')
 
-    if isinstance(cfg.data.test, dict):
-        cfg.data.test.test_mode = True
-    elif isinstance(cfg.data.test, list):
-        for ds_cfg in cfg.data.test:
-            ds_cfg.test_mode = True
-
-    dataset = build_dataset(cfg.data.test)
+    dataset = DATASETS.build(cfg.test_dataloader.dataset)
     confusion_matrix = calculate_confusion_matrix(dataset, results)
     plot_confusion_matrix(
         confusion_matrix,
-        dataset.CLASSES,
+        dataset.METAINFO['classes'],
         save_dir=args.save_dir,
         show=args.show,
         title=args.title,